Keywords: composés volatiles du vin, GC-MS, analyse non ciblée wine volatiles, GC-MS, non-targeted analysis

Titre : Nouvelles approaches par empreinte chromatographique non ciblées des composés volatiles du vin

Résumé :

Contrairement à l'analyse ciblée des composés volatils du vin par chromatographie en phase gazeuse couplée à la spectrométrie de masse (GC-MS), les approches par GC-MS non ciblées prennent en compte les composés connus et inconnus. Ces méthodes sont plus rapides et fournissent une représentation plus complète de la composition de l'échantillon. Bien que plusieurs approches non-ciblées aient été développées, il y a encore une forte demande d'outils automatisés pour le traitement des données, en particulier pour les données multidimensionnelles complexes telles que celles de multiples chromatogrammes GC-MS.

Ce travail visait à développer deux nouvelles approches chimiométriques pour l'analyse des données GC-MS non ciblées. Ces approches prennent en considération les décalages de temps de rétention entre les échantillons et rendent inutile l'intégration des pics. Elles ont été testées avec un jeu de données GC-MS simulées et un jeu de données GC-MS réelles d'échantillons de vin.

De plus, l'une des deux approches GC-MS non ciblée a été combinée à la technique d'analyse sensorielle rapide de "projective mapping". Cette méthodologie a été utilisée pour étudier l'impact de la fermentation malolactique sur des vins issus du cépage Pinotage ainsi que l'effet de l'âge de la vigne, de la turbidité du moût et de la souche de levure sur l'arôme de vins de Riesling expérimentaux.

Fictitious example of a projective mapping sheet of six red wines with freely chosen sensory descriptors from Ultra Flash Profiling. . . . .

2.5

Data structure of projective mapping with Ultra Flash Profiling.

Tasting sheets of K assessors are represented as matrices X k which consist of the x-and y-coordinates of each sample. Citation frequencies of N descriptor groups from Ultra Flash Profiling are represented as matrix D.

3.3

Unfolding of the three-way array (i × j × k ), where i is the number of samples, j is the elution profile (number of scans) and k is the number of mass channels, into a new matrix (i × jk ). . . . . . . . . 3.4 Scores and loadings plot of the first two principal components of the PCA (auto-scaled) on the TICs of the artificial data set (without peak shifts). Samples are coloured according to Table 3.1. . . . . . . vii 3.5 Scores and loadings plot the first two principal components of the PCA (mean-centered) on the TICs of the artificial data set (without peak shifts. Samples are coloured according to Table 3.1. . . . . . . 3.6 Scores and loadings plots of the first two principal components of the PCA (autoscaled) on the unfolded three-way array (Figure 3.3) of the artificial data set (without peak shifts). Samples are coloured according to Table 3.1. . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Scores and loadings plots of the first two principal components of the PCA (mean-centered) on the unfolded three-way array (Figure 3.3) of the artificial data set (without peak shifts). Samples are coloured according to Table 3.1. . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 Loadings of modes one to three of the Tucker3 model on the three-way array of the artificial GC-MS dataset (without peak shifts). Samples are coloured according to Table 3.1. . . . . . . . . . . . . . . . . . .

3.9

Loadings of modes one to three of the Tucker3 model on the three-way array of the artificial GC-MS dataset with shifted peaks. Samples are coloured according to Table 3.1. . . . . . . . . . . . . . . . . . . . .

3.10

Three simulated two dimensional Gas Chromatography Mass Spectrometry (GC-MS) peaks consisting of 22 scans (retention time) and 5 mass channels, represented as the matrices X, Y and Z, and their Sums of Squares and Cross Products (SSCP) matrices XX T , Y Y T and ZZ T (modified from [START_REF] Van Mispelaar | Quantitative analysis of target components by comprehensive twodimensional gas chromatography[END_REF]). . . . . . . . .

3.11

Loadings of the modes one and three of the PARAFAC model on the three-way array of the segmented and mathematically transformed artificial GC-MS dataset without noise and without shifted peaks. Note that mode one and two are identical. Samples are coloured according to Table 3.1. . . . . . . . . . . . . . . . . . . . . . . . . .

3.12

Loadings of the modes one and three of the PARAFAC model on the three-way array of the segmented and mathematically transformed artificial GC-MS dataset with shifted peaks and noise. Note that mode one and two are identical. Samples are coloured according to Table 3.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.13

Scores and saliences (weights of blocks/segments) of CCSWA on the three-way array of the segmented and mathematically transformed artificial GC-MS dataset with shifted peaks and noise. Samples are coloured according to Table 3.1. . . . . . . . . . . . . . . . . . . . .

3.14

Scores and loadings plots of the first two principal components of the PCA (autoscaled) on the final matrix Z of the artificial data set (without peak shifts). Samples are coloured according to Table 3.1. Numbers in the loadings plots refer to the segment and the singular value of the segment (e.g. 1 2: segment 1, second singular value). . viii 3.15 Scores and loadings plots of the first two principal components of the PCA (class centroid centered and scaled to intra-class variance) on the final matrix Z of the artificial data set (without noise and peak shift). Samples are coloured according to Table 3.1. Numbers in the loadings plots refer to the segment and the singular value of the segment (e.g. 1 2: segment 1, second singular value). . . . . . . . . 3.16 Scores and loadings plots of principal components 2 and 3 of the PCA (class centroid centered and scaled to intra-class variance) on the final matrix Z of the artificial data set (without noise and peak shift). Samples are coloured according to Table 3.1. Numbers in the loadings plots refer to the segment and the singular value of the segment (e.g. 1 2: segment 1, second singular value). . . . . . . . . 3.17 Scores and loadings plots of the first two principal components of the PCA (class centroid centered and scaled to intra-class variance) on the final matrix Z of the artificial data set (with noise and peak shift). Samples are coloured according to Table 3.1. Numbers in the loadings plots refer to the segment and the singular value of the segment (e.g. 1 2: segment 1, second singular value). . . . . . . . .

3.18

Scores and loadings plots of principal components 2 and 3 of the PCA (class centroid centered and scaled to intra-class variance) on the final matrix Z of the artificial data set (with noise and peak shift). Samples are coloured according to Table 3.1. Numbers in the loadings plots refer to the segment and the singular value of the segment (e.g. 1 2: segment 1, second singular value). . . . . . . . . . . . . . . . .

3.19

Loadings plots of PARAFAC components three vs. eleven (model with 71 segments); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and Scores and loadings plots of PC1 and PC2 of the PCA on the final matrix Z (Equation 3.16) of the Cabernet Sauvignon data set, where each of the twelve treatments were used as classes for class centroid centering and scaling to intra-class variance. Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271). . . 3.30 Scores and loadings plots of PC3 and PC4 of the PCA on the final matrix Z (Equation 3.16) of the Cabernet Sauvignon data set, where each of the twelve treatments were used as classes for class centroid centering and scaling to intra-class variance. Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271). . . x

3.31

Scores and loadings plots of PC5 and PC6 of the PCA on the final matrix Z (Equation 3.16) of the Cabernet Sauvignon data set, where each of the twelve treatments were used as classes for class centroid centering and scaling to intra-class variance. Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271). . . 3.32 Scores and loadings plots of PC1 and PC2 of the PCA on the final matrix Z (Equation 3.16) of the Cabernet Sauvignon data set, where classes for class centroid centering and scaling to intra-class variance were defined according to the three yeast starter cultures. Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271). . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.33

Scores and loadings plots of the PCA of compounds in segments which had high congruence loadings on components three and eleven of the PARAFAC modell with 71 segments; Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271). . . 3.34 Scores and loadings plots of the PCA of compounds in segments which had high congruence loadings on component one of the PARAFAC model with 71 segments; Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271). . . . . . . . . . . . . . 3.35 Scores and loadings plots of the PCA of compounds in segments which had high congruence loadings on components two and four of the PARAFAC model with 71 segments; Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271). . . 3.36 Scores and loadings plots of PC1 and PC2 of the PCA on all autoscaled compounds of all deconvoluted segments; Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

3.37

Scores and loadings plots of PC1 and PC3 of the PCA on all autoscaled compounds of all deconvoluted segments; Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

xi 3.38 Scores and loadings plots of PC1 and PC3 of the PCA on all compounds of all deconvoluted segments, where class centroid centering and scaling by intra-class variance was applied; Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271). . . 4 MLR coefficients according to the model postulated in Equation 5.1. Factor X 1 : age of vines, factor X 2 : must turbidity, factor X 3 : yeast strain (see Table 5.1 for further details on the factorial design). Response variables are the PARAFAC loadings of the sample mode of each of the components. Significance is indicated as follows: p > 0.05 *, p > 0.01 **, p > 0.001 ***. Adjusted R 2 = adj R 2 . . . . . . . . . Results of MFA of the partial projective mapping (orthonasal evaluation only). Wines in (a) are labeled as follows: OenfKN: Oenoferm Klosterneuburg, X5: Zymaflore X5, EC18: EC1118, O: old vines, Y: young wines, T: turbid must, C: clear must. Frequencies of the aroma descriptor groups of the free choice profiling (b) were included as categorical supplementary variables. Autoscaled peak areas (c, numbers correspond to compounds in Table 5.3) and data from FT-IR (d) were included as continuous supplementary variables. . . . . . . . . A.1 Schematic representation of approach 1. Matrix indices were omited. Note that only the upper triangular matrices of all XX T are used. . B.1

Loadings plots of PARAFAC components one vs. two, where class centroid centering and scaling to intra-class variance was applied to the compilation matrix Y k (Equation 3.9); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271). . . B. 2 Loadings plots of PARAFAC components one vs. four, where class centroid centering and scaling to intra-class variance was applied to the compilation matrix Y k (Equation 3.9); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271). . . B. 3 Loadings plots of PARAFAC components one vs. five, where class centroid centering and scaling to intra-class variance was applied to the compilation matrix Y k (Equation 3.9); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271). . . B. 4 Loadings plots of PARAFAC components seven vs. nine, where class centroid centering and scaling to intraclass variance was applied to the compilation matrix Y k (Equation 3.9); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271). . . Eight largest core entries and their corresponding explained variation (sum of squares) of the [3 3 3]-TUCKER model on the three-way array of the artificial GC-MS data set (sorted in descending order). . . . .

xiii

LIST OF TABLES

3.3

Cabernet Sauvignon wines. Sequential: lactic acid bacteria inoculation after completion of alcoholic fermentation; co-inoculation: lactic acid bacteria inoculation 24 h after yeast inoculation; LAB: lactic acid bacteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.4

Summary of all segments showing high congruence loadings (> 0.5) on PARAFAC components one, two, three, four and eleven and details on the PARAFAC2 models of each segment with corresponding compounds. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4.1

Pinotage wines. Sequential: lactic acid bacteria inoculation after completion of alcoholic fermentation; co-inoculation: lactic acid bacteria inoculation 24 h after yeast inoculation; LAB: lactic acid bacteria.125 4.2 Summary of all segments and their corresponding tentatively identified compounds showing high loadings (congruence loadings > 0.5) on PARAFAC components one, five and six. . . . . . . . . . . . . .

5.1

Structure of the 2×2×3 full factorial design used for the experimental wine making. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5.2

Model matrix for the 2×2×3 full factorial design. Each of the 12 experiments were done in quadruplicate fermentations, resulting in 48 fermentations in total. Coding according to 5.1. . . . . . . . . .

5.3

Summary of all segments and their corresponding tentatively identified compounds showing high loadings (congruence loadings > 0. In contrast to targeted gas chromatography mass spectrometry (GC-MS) analysis of wine volatiles, non-targeted GC-MS approaches take information of known and unknown compounds into account, are faster, inherently more comprehensive and give a more holistic representation of the sample composition. Although several nontargeted approaches have been developed, there is still a great demand for automated data processing tools, especially for complex multi-way data such as chromatographic data obtained from multichannel detectors (e.g. GC-MS chromatograms of multiple samples).

This work therefore aimed at the development of data processing procedures for non-targeted GC-MS analysis of volatile wine compounds. The two developed approaches use basic matrix manipulation of segmented GC-MS chromatograms and PCA or PARAFAC multi-way modelling. The approaches take retention time shifts between samples into account and avoid peak integration. A demonstration of the new fingerprinting approaches is presented using an artificial GC-MS data set and an experimental full-scan GC-MS data set obtained for a set of experimental wines.

xx Results of the new approaches were also compared to a references method.

Furthermore, the combination of one of the developed GC-MS fingerprinting approaches with the fast sensory screening technique projective mapping was exploited The analytical method of choice for the analysis of volatiles is gas chromatography.

Since the introduction of commercial gas chromatography instruments in the late 1950s targeted methods for several wine volatiles have been developed, which always presuppose an a priori known and identified set of compound. Until today, targeted methods, which have the major advantage of accurate quantifications, are mainly used in wine aroma research. This advantage however comes along with the disadvantage of time consuming calibration procedures and the fact that information about differences among samples can only be obtained for a limited number of compounds. The steep rise of metabolomics in the last two decades also inspired wine scientists to use nontargeted approaches for the analysis of wine volatiles. Non-targeted analysis aims to gather qualitative and (semi-)quantitative information on as many compounds as possible in the analysed samples in a short period of time, and thus to provide the researcher with a more holistic view of the composition of samples. Non-targeted strategies are therefore more comprehensive and can be hypothesis generating, as semi-quantitative information on a wide range of different compounds is obtained.

Considering the complexity of the wine matrix which includes hundreds of volatile compounds, non-targeted approaches can be useful to shed new light into the research of wine aroma.

Recent advances in the development of analytical instrumentation enable fast, accurate and cost effective analyses of a large number of samples in numerous domains of analytical chemistry. These improvements in technology made non-targeted screening and fingerprinting analyses of large sample sets possible in the first place, but also lead to a vast increase of more complex data which has to be processed and analysed. The conventional way of addressing these big datasets includes chromatographic preprocessing such as retention time alignment, feature selection (e.g. peak picking) and multivariate modelling of the final peak table. This conventional strategy is also implemented in the available software packages for data analysis of non-targeted chromatographic analysis. Retention time alignment is sometimes difficult to apply and prone to errors (wrong assignment of peaks), while applying feature selection information can be missed, as all peaks missing a certain criteria are not taken into account in further multivariate analysis. These disadvantages of the conventional strategy for non-targeted data analysis indicate the necessity of novel data analysis approaches.

Objectives of this study

The principle objective of this dissertation was the development of a new data analysis approach for non-targeted fingerprinting GC-MS analysis of wine volatiles to overcome drawbacks of conventional methods concerning retention time alignment and feature selection. The alignment issue was solved by segmenting chromatograms and their transformation using linear algebra. By transforming segments of the twodimensional chromatographic signal of each sample into Sums of Squares and Cross Products (SSCP) matrices, a measure for variations of the mass channels and covariations among the mass channels were obtained for each segment. The sums of squares and cross products of the mass channels are not afected by the location of peaks in the segments. Peak shifts among samples do not therefore influence these measures of variation within a mass channel and covariation among mass channels. Based on this transformation, two approaches were developed. Approach one includes further rearrangement of the matrices resulting in a three-way array which can be directly decomposed using the multi-way method Parallel Factor Analysis (PARAFAC). In approach two the SSCP matrices are decomposed in a singular value decomposition (SVD) for each segment and sample and only the first singular values are kept for further principal component analysis (PCA). Both approaches avoid peak alignment and feature selection such as peak integration and were tested on an artificial and a real GC-MS data set. The PARAFAC model in approach one is more difficult to model, but reveals more information on systematic differences among samples and can be used with supervised as well as with unsupervised preprocessing. For approach two supervised preprocessing is inevitable. This approach can therefore only be used when samples can be categorized in classes.

GC-MS

Thesis outline

Chapter 2 -Literature review

This chapter gives an introduction on chromatographic data analysis with an particular focus on conventional and alternative methods for non-targeted data analysis.

Moreover, an overview of volatile wine constituents contributing to the aroma of wine and their analysis using gas chromatography is provided. Finally, a short introduction on rapid sensory profiling techniques is given. The influence of different MLF starter cultures and inoculation modes on the aroma of Pinotage wines is investigated. Results of the differences between the volatile composition of samples obtained from the first approach are combined with the results from fast sensory screening (perceptual mapping) using the multi-block PCA method multiple factor analysis (MFA).

Chapter 5 -Application 2: Full factorial aroma study on the impact of grapevine age, yeast strain and must turbidity on the aroma of Riesling experimental wines

The developed strategy of integrating GC-MS fingerprinting results with those of fast sensory screening from perceptual mapping of wines (Chapter 4) is extended to experimental wine making in full factorial design. Main and interaction effects of the factors grapevine age, yeast strain and must turbidity on the volatile composition and the aroma expression of the Riesling experimental wines were studied.

Chapter 6 -General conclusions

A summary of results and the major findings is given from the development of the non-targeted data analysis approaches to the application of the first approach to the Pinotage and Riesling experimental wines, and data merging of chemical and sensory data.

CHAPTER II

Literature review

From the beginning of commercial Gas Chromatography (GC) in the 1950's until today the principal visual appearance of chromatographic signals has not changed.

The chromatographic signal is visualized as a time series where the detector deflect is represented as peaks corresponding to the eluting substances. In the case of multichannel detectors, such as a mass spectrometer multiple scans are captured in sequence. Then as now, chromatographers have to extract qualitative and quantitative information from chromatograms such as the identities or the concentrations of compounds. Data processing methods have, however, significantly changed in the last 60 years from trivial methods such as cutting out peaks and weighting the cut paper [START_REF] Carroll | Quantitative estimation of peak areas in gas-liquid chromatography[END_REF] to computer modelling of extremely rich data sets of chromatograms from targeted and non-targeted studies in metabolic research 1 nowadays [START_REF] Eliasson | From data processing to multivariate validation-essential steps in extracting interpretable information from metabolomics data[END_REF]. And yet, there is still a lack of fast and automated data processing approaches for chromatographic data, in particular for non-targeted analysis.

The intention of this Chapter is not to discuss chromatographic theory, instru-

1 In metabolic research quantifications (absolute or relative) of one or a few target compounds in a series of biological samples are called metabolite target analysis, while the quantitative (absolute or relative) and qualitative multi-component analysis that define or describe metabolic patterns for a group of metabolically or analytically related metabolites is called metabolic profiling [START_REF] Horning | Metabolic profiles: gas-phase methods for analysis of metabolites[END_REF]. Metabolic fingerprinting is high throughput screening for sample classification by spectroscopic techniques such as NMR or direct infusion mass spectrometry (DIMS). The term metabolomics refers to non-targeted qualitative and quantitative analysis of the complete set of metabolites present in a biological system [START_REF] Dunn | Metabolomics: current analytical platforms and methodologies[END_REF][START_REF] Fiehn | Metabolomics-the link between genotypes and phenotypes[END_REF][START_REF] Koek | Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives[END_REF].

mentation and optimization of separation, which can be found in more dedicated literature [START_REF] Sparkman | Gas Chromatography and Mass Spectrometry: A Practical Guide[END_REF][START_REF] Hübschmann | Handbook of GC/MS: fundamentals and applications[END_REF], but to give an overview of methods for targeted and non-targeted chromatographic data analysis. Moreover, the application of GC and sensory analysis in wine aroma research is reviewed.

Conventional targeted chromatographic data analysis

In conventional quantitative targeted analysis chromatographic peaks are usually fully separated and integrated from the beginning to the end of the peak. The peak areas of samples with known concentrations are used to build a calibration curve and peak areas of unknown samples are related to the calibration curve to determine the accurate concentration of a compound. Peak integration and calculations of concentrations are usually done using commercial software provided from the manufacturer of the chromatographic system. The amount of a compound in a sample can be stated as the accurate concentration (e.g. mg L -1 ). The biggest advantage of accurate targeted quantification is the comparability of results among measured sequences of samples, instruments and laboratories. Disadvantages are that the identity of the component has to be known, standards of known purity have to be available and the calibration procedure is usually time consuming. In some cases, when no standard of known concentration is available, compounds can also be calibrated with reference standards. Such a reference standard is usually a structurally similar compound. Concentrations are then expressed as concentrations calculated relative to the reference standard.

Non-targeted and multivariate chromatographic data analysis

Non-targeted analysis has increasingly gained importance in numerous domains of analytical chemistry such as life science, food science and especially the '-omics' related sciences. In contrast to conventional targeted analysis, non-targeted analysis aims to gather qualitative and quantitative information on as many compounds as possible in the analysed samples in a short period of time, and thus to provide the researcher with a more holistic view of the composition of samples [START_REF] De Vos | Flavour metabolomics: Holistic versus targeted approaches in flavour research[END_REF]. Holistic strategies benefit from the vast amount of information obtained from modern analytical instrumentation. And yet the main challenges associated with non-targeted analysis are data handling and full exploitation of dimensionality of the acquired data. Modern chromatographic instruments such as GC-MS allow automated, reproducible and fast analysis of many samples and are therefore especially suited for non-targeted approaches.

Conventional analysis of non-targeted chromatographic data, such as the common GC-MS metabolomics workflows, generally includes certain steps of data preprocessing such as noise filtering, baseline correction, alignment of peaks, feature selection (e.g. peak detection), identification of peaks, normalization prior to multivariate data analysis and interpretation of the results [START_REF] Koek | Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives[END_REF]. Conventional data analysis approaches and available software packages for non-targeted GC-MS analysis are reviewed in Section 2.2.2. Nevertheless, there is an increasing tendency to use the entire chromatographic profile as a chemical fingerprint containing a unique pattern characteristic for a sample. Benefits and difficulties of fingerprinting approaches are further discussed in Section 2.2.3. But first, an overview on chromatographic data structure is given in Section 2.2.1.

Chromatographic data structure

Conventional non-targeted chromatographic data analysis

The steep rise of metabolomics during the last two decades is closely linked with the continuous development of modern analytical instrumentation, especially the advances in GC-MS and Liquid Chromatography Mass Spectrometry (LC-MS). The necessity of processing more opulent data from more complex instrumentation leads to the development of new algorithms and software tools for non-targeted metabolomics data. Besides commercial software, many free and open source software packages are available today. The probably best known software packages are XCMS [START_REF] Smith | XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification[END_REF], its extensions such as metaMS [START_REF] Wehrens | metaMS: An open-source pipeline for GC-MS-based untargeted metabolomics[END_REF], MZmine [START_REF] Katajamaa | MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data[END_REF][START_REF] Pluskal | MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data[END_REF] and MetAlign [START_REF] Lommen | MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing[END_REF]; many others are listed for instance in [START_REF] Niu | Comparative evaluation of eight software programs for alignment of gas chromatography-mass spectrometry chromatograms in metabolomics experiments[END_REF]; [START_REF] Theodoridis | Liquid chromatography-mass spectrometry based global metabolite profiling: a review[END_REF]; [START_REF] Castillo | Algorithms and tools for the preprocessing of LC-MS metabolomics data[END_REF]. Depending on the chromatographic system and the size of the sample set noise, baseline drift and retention time shifts of peaks among samples are common problems decreasing the quality of chromatograms. Most software for non-targeted chromatographic data analysis address therefore certain preprocessing steps including noise reduction, baseline correction, alignment of peaks, feature selection such as peak As this simple method is not always applicable, the most commonly used baseline correction is a polynomial least square fitting to simulate a blank chromatogram.

Subsequently, the fitted baseline is subtracted from the sample chromatogram. Important for any baseline correction algorithm is to avoid overfitting of the baseline and any elimination and alteration of chemical relevant information. Additionally, factor models can be used to deconvolute baseline and analytical signal in sub-regions of the chromatogram (see Section 2.2.4 for more details).

Noise filtering and smoothing are performed to increase the signal-to-noise ratio by removing high frequency noise from the signal. The most widely used noise reduction technique is the classical Savitzky-Golay method, which fits a least squares polynomial of a given order to a certain window size in the chromatogram [START_REF] Savitzky | Smoothing and differentiation of data by simplified least squares procedures[END_REF]. Other noise reduction methods are based on wavelet smoothing [START_REF] Barclay | Application of wavelet transforms to experimental spectra: smoothing, denoising, and data set compression[END_REF]. Wavelet smoothing algorithms transform the chromatogram into the frequency domain, removing the high-frequency noise, and reverting back to the retention time domain with the result of an smoothed chromatogram.

Small retention time variations among chromatographic runs are generally unavoidable due to column ageing, uncontrollable pressure, flow and temperature fluctuations. Retention time shifts are even more severe in LC analysis, where also variations in the mobile phase have to be considered. When large data sets with multiple compounds are compared with each other, matching of peaks between samples can be impossible without retention time alignment. Besides the linear shift correction i coshift [START_REF] Tomasi | icoshift: An effective tool for the alignment of chromatographic data[END_REF] or the non-linear correlation optimized warping (COW) [START_REF] Skov | Automated alignment of chromatographic data[END_REF][START_REF] Tomasi | Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data[END_REF], many other algorithms for retention time alignment are available [START_REF] Lange | A geometric approach for the alignment of liquid chromatography -mass spectrometry data[END_REF]Sinkov et al., 2011;[START_REF] Nielsen | Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping[END_REF][START_REF] Forshed | Peak alignment of NMR signals by means of a genetic algorithm[END_REF][START_REF] Szymańska | Evaluation of different warping methods for the analysis of CE profiles of urinary nucleosides[END_REF][START_REF] Walczak | Fuzzy warping of chromatograms[END_REF][START_REF] Van Nederkassel | A comparison of three algorithms for chromatograms alignment[END_REF]. Many of the available software packages for non-targeted chromatographic data analysis align peaks after peak detection (for instance in XCMS).

For feature selection, peak picking and deconvolution of chromatogram segments of single samples are used. Most commonly, derivative based approaches are used [START_REF] Felinger | Data analysis and signal processing in chromatography[END_REF] to detect the location of peaks in a chromatogram, but a wide range of other methods are also available [START_REF] Dixon | An automated method for peak detection and matching in large gas chromatographymass spectrometry data sets[END_REF][START_REF] Furbo | Automated peak extraction and quantification in chromatography with multichannel detectors[END_REF][START_REF] Hastings | New algorithms for processing and peak detection in liquid chromatography/mass spectrometry data[END_REF][START_REF] Vivó-Truyols | Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals: Part I: Peak detection[END_REF]. Deconvolution techniques are only rarely implemented in chromatography software. Exceptions are for instance the freely available software Automated Mass Spectral Deconvolution and Identification System (AMDIS) [START_REF] Stein | An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data[END_REF][START_REF] Dromey | Extraction of mass spectra free of background and neighboring component contributions from gas chromatography/mass spectrometry data[END_REF] and the commercial software ChromaTOF (LECO, St. Joseph, MI, USA), which are often used in non-targeted chromatography studies (more on deconvolution in Chapter 2.2.4). For real chromatographic peaks and for deconvoluted peak profiles, either the peak height or the peak area is used as a quantitative measure. The final result for multivariate data analysis is a peak table. Some software packages report multiple entries per metabolite for peaks found for all m/z value, which can be problematic in multivariate analysis of the data [START_REF] Behrends | A software complement to AMDIS for processing GC-MS metabolomic data[END_REF]. Moreover, the quality of the final results are difficult to evaluate because the identity of peaks are not known. For chromatographic preprocessing as well as for peak picking and deconvolution, visual examination of results is very important to avoid any introduction of artefacts by the used algorithms, albeit this validation can be time consuming and cumbersome. Moreover, a good system performance can often avoid the necessity to correct for noise, baseline deviations and peak shifts. [START_REF] Martens | Multivariate analysis of quality[END_REF].

When group classifications are known in advance, more informative supervised multivariate models can be used. Supervised methods take, unlike PCA, intra-class variation (or within class variation) into account. PCA can however be coupled with the class information in order to give classification models by means of Soft Independent Modeling of Class Analogy (SIMCA), which is the first class modelling technique introduced in chemistry [START_REF] Wold | Pattern recognition by means of disjoint principal components models[END_REF][START_REF] Wold | Application of SIMCA multivariate data analysis to the classification of gas chromatographic profiles of human brain tissues[END_REF]. SIMCA defines subspaces for each predefined class by providing a PCA for each class. A new sample is projected and compared to each subspace to evaluate its distance from the corresponding class.

The assignment of the sample is done by comparing the distances of the sample from the class models. Another supervised method often used for classification of samples is Partial Least Squares Discriminant Analysis (PLS-DA). Partial Least Squares (PLS)

was originally designed as a tool for statistical regression and became one of the most commonly used regression techniques in chemistry [START_REF] Wold | Estimation of principal components and related models by iterative least squares[END_REF]. In PLS-DA, the data matrix (peak table) is assigned as the independent variables (X-block) and the class coding is assigned as the dependent variables (Y -block). In a binary classification problem, classes in Y would be simply encoded as a class vector of ones and zeros. PLS-DA essentially searches for latent variables with a maximum covariance with the Y variables. Variation in the data matrix X which is not correlated with the class vector Y can affect the classification results. The interpretation of the results of PLS-DA can therefore significantly be improved by orthogonalizing the model (Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) [START_REF] Trygg | Orthogonal projections to latent structures (O-PLS)[END_REF][START_REF] Tapp | Notes on the practical utility of OPLS[END_REF]), which condenses the Y -block variance into the first latent variable.

Global multivariate modelling of chromatograms: Fingerprinting

Considering an elution profile as a sequence of single measured points, it appears obvious that chromatographic data have a very sparse nature. For instance, the quantitative information content of a chromatographic peak consisting of multiple data points can actually be expressed as a single number (e.g. peak area value or peak height). An ordinary chromatogram with a mass range between 50 and 300 m/z from a one-hour GC-MS run acquired at 4 Hz (four spectra per second) can be represented as a matrix of 14400 scans × 250 masses containing 3600000 data points. If we assume 200 peaks in this chromatogram, peak integration can reduce the data by 18000-fold!

The actual content of information regarding chemical differences among samples in a set of chromatograms is even smaller. Moreover, information on systematic differences among samples in a peak table can often be decomposed into a few latent variables (principal components) using PCA.

Reduction and decomposition of information from chromatographic data can also be achieved by other means. Instead of using feature selection and multivariate modelling of a peak table, as in most software packages for non-targeted chromatographic analysis the chromatographic signal can be processed using mathematical techniques such as decomposition methods on a 'pixel-level', meaning chromatograms are processed in the format of raw detector data points.

The principle idea behind processing chromatograms as raw data points is the inclusion of as much information as possible to the multivariate analysis. In this way it can be avoided to set criteria where in the chromatogram chemically useful information is located (such as signal-to-noise ratio, peak width, peak shape and others), which is necessary when peak picking is applied. Consequently, important information can be missed using peak picking, as all peaks missing a certain criteria are simply not taken into account in further multivariate analysis. Automated peak integration can, depending on the degree of coelution and noise, also be troublesome and often manual intervention is necessary.

Some strategies for modelling chromatographic raw data signals are reported.

Most of them comprise common preprocessing tools such as baseline correction, noise reduction and peak alignment, as well as variable and/or data reduction techniques [START_REF] Ballabio | Classification of GC-MS measurements of wines by combining data dimension reduction and variable selection techniques[END_REF][START_REF] Johnson | Pattern recognition of jet fuels: comprehensive GC×GC with ANOVA-based feature selection and principal component analysis[END_REF][START_REF] Mohler | Comprehensive analysis of yeast metabolite GC×GC-TOFMS data: combining discovery-mode and deconvolution chemometric software[END_REF][START_REF] Borges | Concept for facilitating analyst-mediated interpretation of qualitative chromatographic-mass spectral data: an alternative to manual examination of extracted ion chromatograms[END_REF]Sinkov et al., 2011;Sinkov andHarynuk, 2011, 2013;[START_REF] Teofilo | Sorting variables by using informative vectors as a strategy for feature selection in multivariate regression[END_REF][START_REF] Pierce | Classification of gasoline data obtained by gas chromatography using a piecewise alignment algorithm combined with feature selection and principal component analysis[END_REF][START_REF] Pierce | Fisher ratio method applied to third-order separation data to identify significant chemical components of metabolite extracts[END_REF][START_REF] Adutwum | Unique Ion Filter: A Data Reduction Tool for GC/MS Data Preprocessing Prior to Chemometric Analysis[END_REF][START_REF] Monforte | Chemiomics: Network Reconstruction and Kinetics of Port Wine Aging[END_REF][START_REF] Jonsson | Extraction, interpretation and validation of information for comparing samples in metabolic LC/MS data sets[END_REF][START_REF] Bruce | Evaluation of a protocol for metabolic profiling studies on human blood plasma by combined ultra-performance liquid chromatography/mass spectrometry: From extraction to data analysis[END_REF][START_REF] Rodrigues | Evaluation of beer deterioration by gas chromatography-mass spectrometry/multivariate analysis: A rapid tool for assessing beer composition[END_REF][START_REF] Ferreira | Monitoring Alcoholic Fermentation: An Untargeted Approach[END_REF] or apply weights to variables (Christensen et al., 2005b,a;[START_REF] Christensen | Practical aspects of chemometrics for oil spill fingerprinting[END_REF] prior to multivariate modelling. Similar concepts are also combined with multi-way analysis [START_REF] Durante | A classification tool for N-way array based on SIMCA methodology[END_REF][START_REF] Cocchi | Threeway principal component analysis of the volatile fraction by HS-SPME/GC of aceto balsamico tradizionale of modena[END_REF][START_REF] Durante | Application of N-PLS to gas chromatographic and sensory data of traditional balsamic vinegars of Modena[END_REF]. Retention time alignment is the major disadvantage of all of these approaches, as alignment techniques are sometimes difficult to apply and prone to errors.

A small number of data processing approaches target a new representation of the chromatographic raw data signal by mathematical transformation to avoid retention time alignment of peaks among samples. These mathematical transformations include special correlation measures between data points [START_REF] Danielsson | Rapid multivariate analysis of LC/GC/CE data (single or multiple channel detection) without prior peak alignment[END_REF], the calculation of R V -coefficients [START_REF] Daszykowski | Methods for the exploratory analysis of two-dimensional chromatographic signals[END_REF], dissimilarity [START_REF] Daszykowski | No-alignment-strategies for exploring a set of two-way data tables obtained from capillary electrophoresis-mass spectrometry[END_REF] and distance matrices [START_REF] Zerzucha | A new concept for variance analysis of hyphenated chromatographic data avoiding signal warping[END_REF] for two dimensional chromatograms of samples represented as matrices. The mathematical transformation used for these approaches eliminate information on the retention time of compounds and therefore hamper the identification of compounds responsible for differences between samples.

Another approach taking retention time shifts into account consists of segmentation of the two dimensional chromatograms along the retention time axis and deconvolution of the obtained chromatogram segments using a deconvolution method that takes retention times shifts into account. All samples of each segment are si-multaneously deconvoluted using the two-way method Multivariate Curve Resolution (MCR) [START_REF] Jellema | Deconvolution using signal segmentation[END_REF] or the multi-way method Parallel Factor Analysis 2 (PARAFAC2) (Amigo et al., 2010a) [START_REF] Stein | An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data[END_REF][START_REF] Dromey | Extraction of mass spectra free of background and neighboring component contributions from gas chromatography/mass spectrometry data[END_REF] and the commercial software Chro-maTOF (LECO, St. Joseph, MI, USA). In contrast, high-level technical computing languages such as the freely available R or the commercial MATLAB offer versatile packages and toolboxes for multivariate modelling. Although R and MATLAB are command line driven programs, some packages provide graphical user interfaces (GUI). The great advantage of using computing languages for chromatographic data processing is the flexibility of combining functions from different packages and toolboxes.

Advanced factor models for the mathematical resolution of chromatographic peaks (de Juan and [START_REF] De Juan | Factor analysis of hyphenated chromatographic data: exploration, resolution and quantification of multicomponent systems[END_REF][START_REF] Amigo | ChroMATHography: solving chromatographic issues with mathematical models and intuitive graphics[END_REF][START_REF] Brereton | Tutorial review. Deconvolution of mixtures by factor analysis[END_REF] and multivariate calibration models [START_REF] Escandar | Second-and third-order multivariate calibration: data, algorithms and applications[END_REF][START_REF] Ortiz | Quantitative determination in chromatographic analysis based on n-way calibration strategies[END_REF] have been reviewed

recently. When applied to chromatographic data, PCA is inadequate for finding direct chemically meaningful information, due to the rotational freedom of this bilinear model. This problem can be overcome with more advanced curve-resolution methods or factor models such as MCR-ALS [START_REF] Tauler | Multivariate curve resolution applied to second order data[END_REF], Parallel Factor Analysis (PARAFAC) [START_REF] Bro | PARAFAC. Tutorial and applications[END_REF] and PARAFAC2 [START_REF] Bro | PARAFAC2 -Part II. Modeling chromatographic data with retention time shifts[END_REF]Amigo et al., 2010a[START_REF] Amigo | Solving GC-MS problems with parafac2[END_REF][START_REF] Johnsen | Automated resolution of overlapping peaks in chromatographic data[END_REF]. Considering that each analyte has a distinct pattern, factor models are able to recover the elution and spectral profile. The following criteria must however be at least approximately true. Firstly, according to the Lambert-Beer law the collected spectra of a compound must behave linear to its concentration.

Secondly, the intensity of each spectral point can be assumed to be the sum of the abundances of the analytes forming the mixture in each point of the elution profile.

And lastly, the elution profile must be constant over samples. The shape and position of peaks between samples must not change. Note that PARAFAC2 and some special application of MCR take peak shifts and peak shape changes into account.

Multivariate curve resolution (MCR)

MCR is a bilinear model which is defined for a segment of a single chromatogram X, with J elution time points and K spectral points, as follows:

X = CS T + E, (2.1)
where X is a J × K-matrix, C is a J × N -matrix of elution profiles of N components, S T is a N × K-matrix of spectral profiles and E is J × K-matrix of the residual error matrix.

Usually, an Alternating Least Squares (ALS) algorithm is used for MCR, which requires an initial guess of the number of eluting compounds (chemical rank of X).

This initial guess can be obtained by visual examination, singular values or PCA [START_REF] De Juan | Factor analysis of hyphenated chromatographic data: exploration, resolution and quantification of multicomponent systems[END_REF][START_REF] Maeder | Evolving factor analysis, a new multivariate technique in chromatography[END_REF]. Subsequently, an initial estimation of the spectral or concentration profiles for each compound obtained from e.g. Evolving Factor Analysis (EFA) is used to initialize the ALS procedure. Spectral and elution profiles are iteratively estimated under a series of constraints such as non-negativity, unimodality and sample selectivity (see de [START_REF] De Juan | Factor analysis of hyphenated chromatographic data: exploration, resolution and quantification of multicomponent systems[END_REF]; Bro (1998a) for detailed explanation of constrains) to decrease the extent of possible rotation ambiguities and give physical meaning to the obtained solutions. The algorithm stops when convergence criteria and constraints are met.

The above described deconvolution of a segment of a single chromatogram X can also be extended to multiple I samples X i , as far as the number and the nature of the columns (spectra) are the same for all X i matrices. The arrangement of the matrices and the extended bilinear model can be defined as:

X aug =          X 1 X 2 . . . X I          =          C 1 C 2 . . . C I          S T +          E 1 E 2 . . . E I          = C aug S T + E aug , (2.2)
where X aug is a JI × K-matrix obtained by column-wise augmentation of all X I data matrices. C aug is a JI × N matrix of elution profiles of N components, S T is a N × K-matrix of the spectral profiles and the JI × K residual matrix E aug .

Parallel factor analysis (PARAFAC)

PARAFAC is a multi-way decomposition method which, besides Tucker3, can be seen as a generalization of bilinear PCA to higher order data. PARAFAC can be expressed as a constrained version of Tucker3, and Tucker3 in turn as a constrained version of two-way PCA [START_REF] Kiers | Hierarchical relations among three-way methods[END_REF]. For the matrix x ij and the three-way array

x ijk the PCA model (Equation 2.3), TUCKER3 model (Equation 2.4) and PARAFAC model (Equation 2.5), respectively, are described as follows:

x ij = F f =1 a if b jf + e ij (2.
3)

x ijk = F 1 f =1 F 2 f =1 F 3 f =1 a if b jf c kf g f 1 f 2 f 3 + e ijk (2.4) x ijk = F f =1 a if b jf c kf + e ijk (2.5)
Where F is the number of factors (components), a if , b jf and c kf are elements of the loading matrices A (I×F ) , B (J×F ) and C (K×F ) . g f 1 f 2 f 3 are the elements of the TUCKER3 core array, and e ij and e ijk are elements in the residual matrix E (I×J) and residual array E (I×J×K) , respectively.

The PARAFAC model is visualized in Figure 2.2 and is written in matrix notation as

X i = BD i C T + E i (2.6)
where X i is the i-th frontal slab of the three-way array X, D i is a diagonal matrix with the i-th row of A in its diagonal and E i residuals. is made in multi-way terminology. As PARAFAC requires low-rank trilinear data, chromatograms with many peaks can not be resolved in entirety and must be split into segments containing only a few peaks [START_REF] Bro | On the difference between lowrank and subspace approximation: improved model for multi-linear PLS regression[END_REF][START_REF] Amigo | Solving GC-MS problems with parafac2[END_REF] samples. The PARAFAC2 model is visualised in Figure 2.3 and can be written in matrix notation for the decomposition of an I × J × K three-way array X as

X i = B i D i C T + E i = (P i )D i C T + E i ∀i = 1, . . . , I, (2.7)
where X i is the i-th frontal slab of the three-way array X, D i is a diagonal matrix with the i-th row of A in its diagonal and E i the residuals. C is the loading matrix for the spectral mode and B i the loading matrix of the elution mode for the i-th slab of X modeled as P i H. For F factors (or components) P i is an I × F -matrix and H is a F × F -matrix. P i and H have no direct chemical interpretation, but their product is an estimate of the elution profiles B i .

= +

K I J K R R R J I K I J X E A B i C . . .

Wine aroma

Wine aroma is the perceived odour of a mixture of volatile wine constituents through the human nose. The sensory characteristics which, in fact, are the major quality criterion of a wine, essentially depend on the volatile composition. Dedicated information on volatile constituents in wine is important to the winemaker aiming to produce a product fulfilling consumer sensory expectations. The modulation of wine aroma presupposes a broad understanding of the impact of the different steps of vinification on the composition of aroma compounds [START_REF] Bisson | The present and future of the international wine industry[END_REF][START_REF] Swiegers | Yeast and bacterial modulation of wine aroma and flavour[END_REF].

The word aroma refers to the smell of a wine. Wine aroma can be distinguished into primary, secondary and tertiary aromas. The primary aromas contributing to the varietal character originate from the grapes. The secondary aromas derive from alcoholic and malolactic fermentation and the tertiary aromas are formed during the maturation and ageing process in the barrel and bottle. The term bouquet refers to aromas evolving during maturation in the bottle. The term flavour includes the aroma and taste (sweetness, bitterness, acidity, saltiness, umami) of a wine and is often incorrectly interchanged with the term aroma in popular usage [START_REF] Swiegers | Yeast and bacterial modulation of wine aroma and flavour[END_REF][START_REF] Clarke | Wine flavour chemistry[END_REF]. Natural products such as wine often contain hundreds of volatile compounds with different properties regarding their odour potentials.

The sensory threshold is a very important characteristic of a volatile compound. In complex mixtures the odours of compounds may stay distinct, suppress each other or synergistically create another sensory impression. Even non-volatile compounds or compounds present below their threshold levels can therefore affect the perceived aroma of wine.

The volatile composition of wine consists of several hundreds of compounds. Not all, but a large number of these compounds which originate either from the grapes, wine microbes or the maturation process (wood derived substances from barrels) contribute to wine aroma. The alcoholic fermentation with yeast is particularly important in the formation of wine aroma. The production of major and minor odour active metabolites by yeast from for instance sugar and amino acids, and the conversion of grape derived non-volatile precursor such as glyco-and cystein conjugated compounds are crucial for the development of general wine aroma and in some wines for the varietal aroma character. Lactic acid bacteria used for Malolactic Fermentation (MLF) after or during alcoholic fermentation also influence wine aroma, albeit to a lesser extent than yeast. Lactic acid bacteria also produce aroma active metabolites through the conversion of compounds derived from grapes or alcoholic fermentation.

The major goal of MLF is the reduction of acidity by the conversion of harsh tasting L-malic acid to milder tasting L-lactic acid. Consequently, the style of a wine can be significantly influenced by MLF [START_REF] Swiegers | Yeast and bacterial modulation of wine aroma and flavour[END_REF][START_REF] Clarke | Wine flavour chemistry[END_REF][START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF].

Volatile wine compounds

The most important groups of volatile compounds found in wine are discussed in the following sections.

Alcohols

Varying concentrations of ethanol in wine between 7 and 16 % (v/v) have an impact on the solubility and volatility of aroma active compounds [START_REF] Yu | Ethanol difference thresholds in wine and the influence of mode of evaluation and wine style[END_REF]. Consequently, the ethanol content influences the sensory perception of a wine.

Ethanol plays an important role in the formation of ethyl esters. Methanol, well known for its toxicity, occurs only in very low quantities in wine and originates solely from enzymatic degradation of grape pectin [START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF].

Higher alcohols, sometimes also referred to as fusel alcohols, are aliphatic or branched alcohols with more than two carbons and make up for the largest quantity of volatiles in wine [START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF][START_REF] Sumby | Microbial modulation of aromatic esters in wine: current knowledge and future prospects[END_REF]. These alcohols with higher molecular weights and higher boiling points are produced by yeasts from amino acids via the Ehrlich pathway or from sugars. For example, a higher alcohol can be related to an corresponding amino acid such as 3-methylbutanol from leucine, 2-methylbutanol from isoleucine and 2-methylpropanol from valine. Concentrations produced during fermentation depend on many different factors such as the yeast species and strain, composition of nitrogen containing compounds of the must (e.g. amino acids, ammonia), pH, oxygen levels and temperature. The concentration of these alcohols determine their impact on the sensory perception of wines. Low concentrations (< 300 mg L -1 ) can contribute to the complexity of a wine, while higher levels lead to pungent odours, suppressing the fruitiness and elegance of a wine [START_REF] Clarke | Wine flavour chemistry[END_REF][START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF].

Another important group of alcohols are the C6-alcohols such as hexanol and cis-3hexenol. These compounds are associated with green, herbaceous notes. C6-alcohols occur in high concentrations in wines made from unripe grapes. The corresponding aldehydes of these alcohols are degradation products of linoleic and linolenic acids [START_REF] Clarke | Wine flavour chemistry[END_REF][START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF]. The unsaturated secondary alcohol 1-octen-3-ol is particularly found in botrytized wines [START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF][START_REF] Rapp | Wine aroma[END_REF]. This compound has an odour of mushrooms and is a well-known fungal metabolite also related to molds such as Aspergillus and Penicillium [START_REF] Kaminski | Volatile flavor compounds produced by molds of Aspergillus, Penicillium, and Fungi imperfecti[END_REF].

Esters

Esters are the primary source of fruity aromas and the second major constituents of wine volatiles after fusel alcohols. The composition of esters and synergistic effects influence various fruity notes. Esters are therefore also very important for the overall sensory perception of a wine [START_REF] Lytra | Impact of perceptive interactions on red wine fruity aroma[END_REF][START_REF] Lytra | Study of sensory interactions among red wine fruity esters in a model solution[END_REF]. Wine esters are either formed enzymatically or evolve during wine ageing by chemical esterification of alcohols and acids. Enzymatic ester synthesis by yeast is catalysed by esterases, lipases and alcohol acetyltransferases [START_REF] Sumby | Microbial modulation of aromatic esters in wine: current knowledge and future prospects[END_REF].

The quantity of esters formed during fermentation depends on the activity of the involved enzymes, the yeast strain, nutrition status, fermentation temperature, and the degree of must clarification. Esters which were produced during fermentation in excess of their equilibrium hydrolyse during wine ageing, as the chemical esterification and hydrolysis of ester is an equilibrium reaction. Ester hydrolysis is favoured at high temperature and low pH. In fact, depending on this reaction the equilibrium levels of some esters increase during wine ageing. Branched fatty acid ethyl esters tend to increase as a function of time, since they are present at low levels after fermentation [START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF][START_REF] Sumby | Microbial modulation of aromatic esters in wine: current knowledge and future prospects[END_REF].

The possible variety of esters is enormous considering the large number of different acids and alcohols in wine. The wide range of esters in wine can be grouped according to similar structure or physiochemical properties as follows: major aliphatic ethyl esters (even number of carbons), aliphatic ethyl esters (odd number of carbons), ethyl esters of branched aliphatic acids, aromatic esters, acetates of higher alcohols, methyl esters, minor isoamyl esters, and others [START_REF] Antalick | Development, validation and application of a specific method for the quantitative determination of wine esters by headspace-solid-phase microextraction-gas chromatography-mass spectrometry[END_REF].

Fatty acids

Acids contributing primarily to the titratable acid of wine namely tartaric acidity, malic acid and lactic acid are not volatile. The concentrations of these acids can however impact the aroma by playing a role in the release of aroma compounds from wine. Volatile acidity (VA) consists of approximately 90 % acetic acid. Yeast produces olfactorily imperceptible amounts of acetic acid. Perceptiple amounts of acetic acid can however originate from microbial spoilage, in particular from some lactic acid bacteria and acetobacter species. Moreover, increased levels of propanoic acids, butanoic acids and especially 3-methylbutanoic acid (isovaleric acid) are associated with microbial contamination. Hexanoic, octanoic and decanoic acid derive from yeast metabolism. In high concentrations, these compounds can lead to rancid, pungent, cheese and fat-like odours and are considered to cause stuck fermentations [START_REF] Swiegers | Yeast and bacterial modulation of wine aroma and flavour[END_REF][START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF][START_REF] Francis | Determining wine aroma from compositional data[END_REF].

Carbonyl compounds

Aldehydes are oxidation products of primary alcohols. Acetaldehyde (ethanal) is the most abundant carbonyl compound in wine. The formation of acetaldehyde occurs during alcoholic fermentation and depends mainly on must composition, must clarification and aeration status. Moreover, acetaldehyde can increase over time due to oxidation of ethanol and activity of spoilage yeast [START_REF] Bennetzen | The primary structure of the Saccharomyces cerevisiae gene for alcohol dehydrogenase[END_REF][START_REF] Denis | mRNA levels for the fermentative alcohol dehydrogenase of Saccharomyces cerevisiae decrease upon growth on a nonfermentable carbon source[END_REF][START_REF] Fleet | Wine microbiology and biotechnology[END_REF]. Aldehydes in general react with sulphur dioxide (formation of bisulfite adducts). Consequently, insufficient addition of sulphur dioxide during the wine making process leads to elevated levels of free acetaldehyde, which is negatively perceived as 'flatness'. Aldehydes are also associated with oxidized aroma notes in wines, such as 'cut-apple' and 'nutty' odours. During vinification acetaldehyde also plays an important role as an binding partner for phenolic compounds and has therefore an impact on the formation of color pigments and tannins [START_REF] Boulton | The copigmentation of anthocyanins and its role in the color of red wine: a critical review[END_REF][START_REF] Timberlake | Interactions between anthocyanins, phenolic compounds, and acetaldehyde and their significance in red wines[END_REF]. Analogous to the C6-alcohols, C6-aldehydes such as hexanal and cis-3-hexenal contribute to 'green', 'herbaceous' odours. Aromatic, wood derived aldehydes such as vanillin and cinnamic aldehyde can contribute to tertiary aromas of wine.

Ketones are oxidation products of secondary alcohols. The most important compound in this class formed in wine is the diketone diacetyl (2,3-butandione). While yeast is responsible for the production of large amounts of diacetyl during beer fermentation, lactic acid bacteria are the main source of this vicinal diketone in wine, albeit wine yeasts also produce insignificant amounts of this compound. Malolactic fermentation can be conducted in a controlled manner, but undesired activity of spontaneous lactic acid bacteria flora can lead to spoilage of the wine. The sensory impact of diacetyl in wine is described as sweet, buttery and butterscotch. These odours are perceived as pleasant in low concentrations, higher concentrations however, lead to an objectionable off-flavour. Diacetyl production of lactic acid bacteria during malolactic fermentation can be controlled by several factors such as the malolactic bacteria strain, inoculation dosage, temperature, pH, citric acid content and sulphur dioxide concentrations used during vinification. The latter results from the above mentioned reaction of carbonyl compounds with bisulfid ions. Diacetyl can be reduced to 2,3-butanediol in wine conditions. 2,3-butanediol has a much higher odour threshold then diacetyl, which is rarely exceeded in wine [START_REF] Bartowsky | The buttery attribute of wine -diacetyl -desirability, spoilage and beyond[END_REF][START_REF] Clarke | Wine flavour chemistry[END_REF][START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF].

Terpenes

C5-Isoprene units are the building blocks of terpenes. The most important classes of terpenes are the monoterpenes consisting of two isoprene units, sequiterpenes consisting of three isoprene units and the C13-norisoprenoids. Chemically modified terpenes through oxidation or rearrangement are called terpenoids. In the following discussion the term terpene will be used to include all terpenoids for the sake of simplicity [START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF].

A France. Terpenes may contribute to the aromas of non-muscat varieties as well.

In other very popular grape cultivars such as Sauvignon blanc, or particularly red varieties such as Cabernet Sauvignon, Merlot, Cabernet franc and Syrah, terpenes are usually present under their olfactory thresholds and do therefore not play a significant role in the aromas of these cultivars [START_REF] Marais | Terpenes in the aroma of grapes and wines: a review[END_REF][START_REF] Clarke | Wine flavour chemistry[END_REF][START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF]. Due to higher water solubility, glycosides serve as carriers for the transport and accumulation of the corresponding aglycones in plants. Muscat grape varieties have a particularly large ratio of glycosylated terpenols to free from, whereas this ratio for non-muscat cultivars is approximately 1:1. Aglycones of glycosides can be realeased either enzymatically or by acid hydrolysis, whereas the latter plays a minor role in wine. Enzymes with glycosidic activity responsible for the liberation of aroma compounds are mainly sourced from yeasts, but also bacteria and grapes. Oenological enzymes used for clarification can also have glycosidic side activity [START_REF] Clarke | Wine flavour chemistry[END_REF][START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF][START_REF] Black | Terpenoids and their role in wine flavour: recent advances[END_REF].

C13-norisoprenoids are degradation products of carotenoids and can be grouped into megastigmanes and non-megastigmanes. Two examples of megastigmanes with a very low perception threshold of only several µg L -1 are β-damascenone and βionone, which contribute to 'fruity' and 'flowery' notes in wine [START_REF] Sefton | Norisoprenoids in Vitis vinifera white wine grapes and the identification of a precursor of damascenone in these fruits[END_REF][START_REF] Mendes-Pinto | Carotenoid breakdown products the -norisoprenoidsin wine aroma[END_REF]. The most important non-megastigmane is 1,1,6-trimethyl-1,2dihydronaphtalene (TDN) which is responsible for the distinct 'kerosene' odour in Riesling and contributes to the ageing bouquet of Riesling wines [START_REF] Winterhalter | Volatile C13-norisoprenoid compounds in Riesling wine are generated from multiple precursors[END_REF][START_REF] Winterhalter | of 2,6,10,10-tetramethyl-1-oxaspiro [4.5] dec-6-ene-2,8-diol rationalizing the origin of TDN and related C13 norisoprenoids in Riesling wine[END_REF].

Sulphur and nitrogen containing compounds

The majority of volatile sulphur containing compounds in wine are associated with reductive off-flavours. Some thiols however have positive sensory characteristics and contribute to the varietal aroma of certain grape varieties. Negatively perceived volatile sulphur compounds are either directly or indirectly linked to yeast metabolism. Residues from sulphur containing spray agents and thermal or photochemical reactions can also be a source of volatile sulphur compounds. Volatile sulphur compounds are often divided into low-boiling and high-boiling compounds [START_REF] Swiegers | Yeast and bacterial modulation of wine aroma and flavour[END_REF].

High concentrations of the low-boiling sulphur compounds methanethiol, ethanethiol and particularly hydrogen sulphide lead to reductive off-flavours such as 'rotten egg' and 'sewage'. Hydrogen sulphide is a yeast metabolite formed intracellularly by the reduction of sulphates and the metabolisation of sulphur containing amino acids such as cysteine and methionine. The production of hydrogen sulphide is therefore strongly linked to nitrogen metabolism. High production of hydrogen sulphide due to nitrogen deprivation during fermentation can be avoided by the addition of ammonium sulphate in the early stages of fermentation. Hydrogen sulphite can react with methanol and ethanol to produce methanethiol and ethanethiol [START_REF] Lambrechts | Yeast and its importance to wine aromaa review[END_REF][START_REF] Swiegers | Yeast and bacterial modulation of wine aroma and flavour[END_REF]. Dimethyl sulphide (DMS) is a rare example of a positively associated low-boiling volatile sulphur compound. This sulphur compound is formed by yeast, it evolves during ageing and is therefore considered to contribute to the bouquet [START_REF] De Mora | The formation of dimethyl sulphide during fermentation using a wine yeast[END_REF][START_REF] Ferreira | Influence of some technological parameters on the formation of dimethyl sulfide, 2-mercaptoethanol, methionol, and dimethyl sulfone in port wines[END_REF][START_REF] Picard | Involvement of dimethyl sulfide and several polyfunctional thiols in the aromatic expression of the aging bouquet of red Bordeaux wines[END_REF].

High boiling sulphur volatiles are only of minor importance to wine aroma, although methionol is an exception. The deamination and decarboxylation of methionine according to the Ehrlich pathway, results in the formation of methionol, which is perceived as 'cauliflower' aroma in higher concentrations [START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF].

The varietal thiols 4-mercapto-4-methyl-pentan-2-one (4MMP), 3-mercaptohexan-1-ol (3MH), 3-mercapto-3-methyl-butan-1-ol (3MMB), 4-mercapto-4-methyl-pentan-1-ol (4MMPOH) and 3-mercaptohexanolacetate (3MHA) have been identified as key molecules in some grape varieties. The varietal aroma of Sauvignon blanc is particularly determined by these thiols (besides the methoxypyrazines). Other varieties such as the white cultivars Semillon, Scheurebe and Riesling, or the red cultivars Cabernet Sauvignon, Merlot and Pinot noir among others also contain varying amounts of these thiols. The single compounds have different odour expressions of 'boxtree' and 'passion fruit' (4MMP); 'passion fruit', 'grapefruit', 'gooseberry' and 'guava' (3MHA & 3MH); and 'cooked leeks' (3MMB) [START_REF] Swiegers | The influence of yeast on the aroma of Sauvignon Blanc wine[END_REF][START_REF] Roland | Varietal thiols in wine: discovery, analysis and applications[END_REF].

Similar to terpenols, these thiols result from the cleavage of odourless precursors by yeast enzymes during alcoholic fermentation, whereas the nonvolatile precursors are not glycosides, but S-cysteine conjugates. It is assumed that yeast originated β-lyases are responsible for the non-quantitative release of these thiols during fermentation [START_REF] Tominaga | A New Type of Flavor Precursors in Vitis v inifera L. cv. Sauvignon Blanc: S-Cysteine Conjugates[END_REF][START_REF] Peyrot Des Gachons | Measuring the aromatic potential of Vitis vinifera L. Cv. Sauvignon blanc grapes by assaying Scysteine conjugates, precursors of the volatile thiols responsible for their varietal aroma[END_REF]. Alternatives to the classical pathway from cysteine conjugates, such as the 1,4-addition of hydrogen sulphide to conjugated carbonyl compounds (e.g. E -hex-2-enal), have also been described [START_REF] Schneider | Evidence for an alternative biogenetic pathway leading to 3-mercaptohexanol and 4-mercapto-4-methylpentan-2-one in wines[END_REF].

With few exceptions, volatile nitrogen compounds are of minor importance regarding the aroma of wine. In Cabernet Sauvignon, Sauvignon blanc and Caber-net franc, the grapevine metabolites 3-alkyl-2-methoxypyrazines matter particularly.

The compounds 3-isopropyl-2-methoxypyrazine, 3-isobutyl-2-methoxypyrazine and 3sec-butyl-2-methoxypyrazine have very low perception thresholds and are the most studied in this group contributing to aromas of 'green bell pepper', 'asparagus' and 'earthy'. Undesired herbaceous notes in Cabernet Sauvignon and Cabernet franc wines made from unripe grapes are attributed to 2-methoxy-3-isobutylpyrazine. 2methoxy-3-isobutylpyrazine is located in the grape skins and therefore increases during fermentation and maceration. On the other hand, herbaceous notes associated with 2-methoxy-3-isobutylpyrazine such as 'green bell pepper' can be desirable in Sauvignon blanc wines [START_REF] Allen | Contribution of methoxypyrazines to Sauvignon blanc wine aroma[END_REF][START_REF] Lacey | Methoxypyrazines in Sauvignon blanc grapes and wines[END_REF][START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF].

Some thiazoles and oxazoles are thought to contribute to the ageing aroma of wine. Athough the mechanisms of the formation of these compounds are not yet fully understood, some might be formed in a Maillard-type reaction between carbonyl or dicarbonyl compounds and amino acids. [START_REF] Keim | Method for determining nitrogenous heterocycle compounds in wine[END_REF][START_REF] Marchand | Approaches to wine aroma: release of aroma compounds from reactions between cysteine and carbonyl compounds in wine[END_REF][START_REF] Marchand | Possible mechanism for involvement of cysteine in aroma production in wine[END_REF][START_REF] Marchand | The Cysteine Reaction with Diacetyl under Wine-Like Conditions: Proposed Mechanisms for Mixed Origins of 2-Methylthiazole, 2-Methyl-3-thiazoline, 2-Methylthiazolidine, and 2,4,5-Trimethyloxazole[END_REF]

Other volatile compounds

Lactones and furans are compounds of different origin which influence wine aroma.

Lactones are formed by intra molecular condensation of a hydroxy and a carboxy group resulting in an cyclic ester. Saturate γ-lactones are also called dihydrofurans.

Lactones can be arise during fermentation by rearrangement of hydroxycarboxylic acid obtained from deamination and decarboxylation of amino acids. Some lactones are associated to specific grape varieties. For instance, 2-vinyl-dihydrofuran-2-one is present in Riesling and Muscat wines and 2,5-dimethyl-4-hydroxy-3(2H)-furanone (furaneol) can be found in Merlot and Vitis lambrusco wines. The sotolon (3-hydroxy-4,5-dimethyl-2(5H)-furanone) is linked to botrytized and fortified wines and marker for premature oxidative ageing of wine. Sotolon can be formed by condensation of α-keto butyric acid and acetaldehyde. The 'oak lactones' or 'whiskey lactones', which are the cis-and trans-isomers of 3-methyl-γ-octalactone, contribute to the 'oaky' aroma of wines vinified in barrels. Other compounds of this class may arise from saccharide degradation and through the Maillard reaction [START_REF] Muller | Lactones in wines -a review[END_REF][START_REF] Clarke | Wine flavour chemistry[END_REF][START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF].

Another group of important wine compounds are volatile phenols. The four compounds 4-vinylphenol, 4-vinylguaiacol, 4-ethylphenol and 4-ethylguaiacol are predominantly associated with objectionable 'phenolic' character. 4-ethylphenol and 4-vinylphenol are related with odour descriptors as 'barnyard', 'sweaty saddle' and 'medicinal', 'Band Aid', respectively. These odours are mainly perceived as unpleasant, while 4-vinylguaiacol and 4-ethylguaiacol have positive odours of carnations and 'smoky', 'spicy', respectively [START_REF] Chatonnet | Influence of polyphenolic components of red wines on the microbial synthesis of volatile phenols[END_REF]. These compounds are formed through enzymatic degradation of the cinnamic acids p-coumaric and ferulic acid by yeast (Saccharomyces cerevisiae) derived cinnamate decarboxylase. Other phenolic compounds such as procyanidins inhibit cinnamate decarboxylase activity resulting in lower levels of 4-vinylphenols in red wines compared to white wines. The concentration of this compound in white wine depends on the activity of cinnamate decarboxylase and concentration of the precursors, which in turn vary among grape cultivars [START_REF] Chatonnet | Influence of polyphenolic components of red wines on the microbial synthesis of volatile phenols[END_REF][START_REF] Du Toit | Microbial spoilage and preservation of wine: using weapons from natures own arsenal-a review[END_REF][START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF].

Volatile phenols can also derive from spoilage by Brettanomyces/Dekkera yeasts, which express a cinnamate decarboxylase which is not inhibited by phenolic compounds resulting in the conversion of large quantities of cinnamic acids to 4-vinylphenol and 4-vinylguaiacol. These spoilage yeasts also produce vinylphenol reductase, which is absent in Saccharomyces cerevisiae, catalysing further reduction of 4-vinylphenol and 4-vinylguaiacol to 4-ethylphenol and 4-ethylguaiacol. Proper sulphur dioxide management during vinification can prevent growth of these spoilage yeasts [START_REF] Chatonnet | The influence of Brettanomyces/Dekkera sp. yeasts and lactic acid bacteria on the ethylphenol content of red wines[END_REF][START_REF] Chatonnet | Influence of polyphenolic components of red wines on the microbial synthesis of volatile phenols[END_REF][START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF].

Gas chromatography in wine analysis

Wine aroma is the perceived scent of wine, which in turn is the detection of volatile wine constituents by means of the olfactory nerves in the human nose. Gas chromatography is the most suitable analytical technique for the analysis of volatile compounds and therefore, the most widely used method for the analysis of aroma compounds in wine. Targeted analysis of wine aroma compounds are commonly conducted. A targeted approach always presuppose an a priori defined set of compounds of interest. In numerous domains of analytical sciences including wine analysis, non-targeted strategies have recently gained more attention. Non-targeted approaches focus on the extraction and analysis of as many compounds as possible in the analysed samples to obtain a more comprehensive picture of the sample composition. Targeted and non-targeted approaches to gas chromatography have been more generally discussed in Sections 2.1 and 2.2, respectively. In the next section, targeted and non-targeted analysis is specifically discussed in a wine context.

Conventional targeted analysis of wine volatiles

In principle, all commercially available separation columns, injection systems, and detectors are used for the analysis of wine volatiles using gas chromatography. Commonly more polar column phases such as polyethylene glycol (PEG) or modified PEG (WAX) [START_REF] Ferreira | Fast and quantitative determination of wine flavor compounds using microextraction with Freon 113[END_REF][START_REF] Bonino | Aroma compounds of an Italian wine (Ruché) by HS-SPME analysis coupled with GC-ITMS[END_REF][START_REF] Boido | The effect of bacterial strain and aging on the secondary volatile metabolites produced during malolactic fermentation of Tannat red wine[END_REF][START_REF] Ugliano | Changes in the concentration of yeast-derived volatile compounds of red wine during malolactic fermentation with four commercial starter cultures of Oenococcus oeni[END_REF] are preferred due to the diverse nature of volatile wine constitutes, but also non-polar phases such as polydimethylsiloxane (PDMS) [START_REF] Escudero | Analytical characterization of the aroma of five premium red wines. Insights into the role of odor families and the concept of fruitiness of wines[END_REF][START_REF] Sánchez-Palomo | Rapid determination of volatile compounds in grapes by HS-SPME coupled with GC-MS[END_REF] or enantio-selective (cyclodextrin based) phases for chiral separations are used [START_REF] Fernandes | Different multidimensional chromatographic approaches applied to the study of wine malolactic fermentation[END_REF].

Sample preparation is in general a crucial point in GC analysis. For the analysis of wine volatiles interfering matrix constituents such as water, alcohol and non-volatiles have to be taken into account. The selection of a sample preparation technique depends mainly on the physiochemical properties (e.g. polarity) and the concentration of analytes. Aroma compounds in wine are often loosely differentiated between major and minor volatiles. Major volatiles are mainly higher alcohols, some esters and fatty acids. Liquid-Liquid Extraction (LLE) with for instance diethyl ether [START_REF] Louw | The effect of polysaccharide-degrading wine yeast transformants on the efficiency of wine processing and wine flavour[END_REF][START_REF] Lilly | Effect of increased yeast alcohol acetyltransferase activity on flavor profiles of wine and distillates[END_REF], dicloromethane [START_REF] Selli | Aroma components of cv. Muscat of Bornova wines and influence of skin contact treatment[END_REF][START_REF] Perestrelo | Analytical characterization of the aroma of Tinta Negra Mole red wine: Identification of the main odorants compounds[END_REF][START_REF] Mallouchos | Wine fermentations by immobilized and free cells at different temperatures. Effect of immobilization and temperature on volatile by-products[END_REF] or Freon 113 [START_REF] Ferreira | Fast and quantitative determination of wine flavor compounds using microextraction with Freon 113[END_REF][START_REF] Muñoz | Biological aging of sherry wines under periodic and controlled microaerations with Saccharomyces cerevisiae var. capensis: Effect on odorant series[END_REF] and Solid Phase Microextraction (SPME) are the most commonly used sample preparation techniques for these compounds present in high concentrations in wine. The analysis of minor volatiles can be very difficult in terms of the extraction, enrichment and detection of analytes. Solid Phase Extraction (SPE) meets these requirements for the trace analysis of minor compounds, as it is applicable to a wide range of compounds due to the availability of different commercial phases. Usually, reversed-phase C18 [START_REF] Lukić | Determination of volatile compounds in grape distillates by solid-phase extraction and gas chromatography[END_REF], Lichrolute EN [START_REF] Loscos | Release and formation of varietal aroma compounds during alcoholic fermentation from nonfloral grape odorless flavor precursors fractions[END_REF][START_REF] Lopez | Determination of minor and trace volatile compounds in wine by solid-phase extraction and gas chromatography with mass spectrometric detection[END_REF] and styrene diviniylbenzene phases [START_REF] Palomo | Aroma enhancement in wines from different grape varieties using exogenous glycosidases[END_REF] are used.

The major problem with LLE and SPE are hazardous properties of organic solvents used which determined by their molecular structure can be toxic, flammable, carcinogenic and/or neurotoxic. Furthermore, all organic solvents are environmentally hazardous, especially the greenhouse gas, Freon. Solvent free techniques are therefore preferred and gain more and more popularity. SPME is a solvent free and fully automatable alternative to LLE and SPE. Fibres with different characteristics have been used for the analysis of wine volatiles namely: PDMS [START_REF] Riu-Aumatell | Development of volatile compounds of cava (Spanish sparkling wine) during long ageing time in contact with lees[END_REF][START_REF] Alves | Characterization of the aroma profile of Madeira wine by sorptive extraction techniques[END_REF], carboxen/PDMS (CAR/PDMS) [START_REF] Piñeiro | Characterisation of volatile fraction of monovarietal wines: Influence of winemaking practices[END_REF], PDM-S/Divinylbenzene (PDMS/DVB) (Sánchez- [START_REF] Palomo | Aroma enhancement in wines from different grape varieties using exogenous glycosidases[END_REF], DVB/CAR/PDMS (Sánchez- [START_REF] Palomo | Aroma enhancement in wines from different grape varieties using exogenous glycosidases[END_REF], polyethyleneglycol/DVB (PEG/DVB) [START_REF] Flamini | Monitoring of the principal carbonyl compounds involved in malolactic fermentation of wine by solid-phase microextraction and positive ion chemical ionization GC/MS analysis[END_REF] and polyacrylate (De la Calle [START_REF] De La Calle | Investigations on wine bouquet components by solid-phase microextration-capillary gas chromatography (SMPE-CGC) using different fibers[END_REF]. A more recent development of a solvent free sample preparation technique suitable for the analysis of wine volatiles is stir bar sorptive extraction (SBSE) [START_REF] Hayasaka | Application of stir bar sorptive extraction for wine analysis[END_REF][START_REF] Zalacain | Analysis of wine primary aroma compounds by stir bar sorptive extraction[END_REF][START_REF] Weldegergis | Analysis of volatiles in Pinotage wines by stir bar sorptive extraction and chemometric profiling[END_REF]. SBSE shows significant increase in sensitivity compared to SPME due to the higher phase volume, and is therfore also suitable for the analysis of trace compounds [START_REF] Sandra | Stir bar sorptive extraction applied to the determination of dicarboximide fungicides in wine[END_REF][START_REF] Zalacain | Stir bar sorptive extraction for the analysis of wine cork taint[END_REF]. Besides PDMS, a more polar mixed phase of ethylene glycol and PDMS is available since recently, which facilitates the extraction of more polar wine volatiles [START_REF] Elpa | Development of a new stir bar sorptive extraction method for the determination of medium-level volatile thiols in wine[END_REF].

Non-targeted approaches to wine volatiles

Inspired by the new field of metabolomics the number of wine related studies comprising non-targeted strategies have steadily increased in the last years. A variety of methodologies for non-targeted analysis of wine volatiles using GC-MS have been applied to several oenological and viticultural questions. A review outlining a variety of reported studies on wine metabolite profiling is given by [START_REF] Atanassov | Wine metabolite profiling: possible application in winemaking and grapevine breading in bulgaria[END_REF].

To give a brief overview on the applicability of non-targeted GC-MS analysis to wine, some of the more recent publications are summarised in the following. The combination of non-targeted GC-MS and NMR analysis was also used to unravel metabolites in grape juice that affect the production of varietal thiols in Sauvignon blanc wines by [START_REF] Pinu | Sauvignon blanc metabolomics: grape juice metabolites affecting the development of varietal thiols and other aroma compounds in wines[END_REF].

Besides GC-MS other analytical techniques have been used for non-targeted analysis of wine constituents. As in classical metabolomics, LC-MS [START_REF] Tarr | A metabolomics based approach for understanding the influence of terroir in Vitis Vinifera L[END_REF][START_REF] Arapitsas | Studying the effect of storage conditions on the metabolite content of red wine using HILIC LC-MS based metabolomics[END_REF][START_REF] Arapitsas | The influence of storage on the chemical age of red wines[END_REF][START_REF] Arapitsas | A metabolomic approach to the study of wine micro-oxygenation[END_REF][START_REF] Roullier-Gall | Integrating analytical resolutions in non-targeted wine metabolomics[END_REF][START_REF] Arbulu | Untargeted metabolomic analysis using liquid chromatography quadrupole time-offlight mass spectrometry for non-volatile profiling of wines[END_REF][START_REF] Toffali | Novel aspects of grape berry ripening and post-harvest withering revealed by untargeted LC-ESI-MS metabolomics analysis[END_REF] and NMR [START_REF] Lopez-Rituerto | Investigations of La Rioja terroir for wine production using 1H NMR metabolomics[END_REF][START_REF] Laghi | Metabonomic investigation by 1H-NMR to discriminate between red wines from organic and biodynamic grapes[END_REF][START_REF] Rochfort | Sensory attributes of wine influenced by variety and berry shading discriminated by NMR metabolomics[END_REF] are commonly used analytical technique. These techniques are here, however, not further discussed.

Rapid sensory profiling of wine

The objective of sensory profiling is to provide a visualisation of differences between samples perceived by a taster in the form of a product map. The quality of these maps depend on certain criteria such as the repeatability of blind duplicates, representation of descriptive attributes of the samples, interpretability and clear representation of the results. The outcome should be useful to either confirm a hypothesis or postulate a new hypothesis. Sensory profiles of multiple samples meeting these aforementioned requirements can be obtained using conventional sensory profiling methods, such as Quantitative Descriptive Analysis (QDA) [START_REF] Stone | Sensory Evaluation by Quantitative Descriptive Analysis[END_REF], in combination with multivariate data analysis (e.g. PCA). These conventional techniques, however, require intensive training of panellists and are therefore time-consuming.

Rapid descriptive methods provide a view on the sensory differences among samples similarly to conventional profiling methods. Labour-intensive panel training is, however, omitted or reduced, resulting in a dramatic decrease of the total analysis time [START_REF] Risvik | Projective mapping: A tool for sensory analysis and consumer research[END_REF][START_REF] Risvik | Evaluation of sensory profiling and projective mapping data[END_REF]. This saving of time comes with the cost of sacrificing quantitative data of defined sensory attributes. When defined sensory descriptors as in conventional sensory profiling are used, information on the importance of other/different attributes in the overall perception of panellists is not obtained. Some rapid sensory profiling methods overcome this problem by allowing the taster to more freely decide how to indicate differences between samples. These faster alternatives, such as perceptual mapping (e.g. 'napping') with Ultra Flash Profiling, provide citation frequencies of sensory descriptors freely chosen by the assessors, which explain sensory differences in the sample set (Pagès, 2005a;[START_REF] Delarue | Sensory mapping using Flash profile. Comparison with a conventional descriptive method for the evaluation of the flavour of fruit dairy products[END_REF][START_REF] Cartier | Sorting procedure as an alternative to quantitative descriptive analysis to obtain a product sensory map[END_REF][START_REF] Dehlholm | Rapid descriptive sensory methods-comparison of free multiple sorting, partial napping, napping, flash profiling and conventional profiling[END_REF]. The increasing number of publications on the application of rapid sensory profiling techniques to food stuffs and beverages testify that these methods have recently gained more popularity [START_REF] Perrin | Comparison of three sensory methods for use with the Napping® procedure: Case of ten wines from Loire valley[END_REF]Nestrud andLawless, 2008, 2010;[START_REF] Kennedy | Evaluation of replicated projective mapping of granola bars[END_REF][START_REF] Ross | Impact of serving temperature on sensory properties of red wine as evaluated using projective mapping by a trained panel[END_REF][START_REF] Torri | Projective mapping for interpreting wine aroma differences as perceived by naïve and experienced assessors[END_REF][START_REF] Santos | Ultra-flash profile and projective mapping for describing sensory attributes of prebiotic mortadellas[END_REF].

Two recent reviews give a comprehensive overview on theoretical background, implementations, advantages and disadvantages and comparison of different rapid descriptive methods [START_REF] Varela | Sensory profiling, the blurred line between sensory and consumer science. A review of novel methods for product characterization[END_REF][START_REF] Valentin | Quick and dirty but still pretty good: A review of new descriptive methods in food science[END_REF]. The expeditious means of these novel sensory profiling techniques are very well elucidated by the expressive title of one of these reviews: Quick and dirty but still pretty good: a review of new descriptive methods in food science [START_REF] Valentin | Quick and dirty but still pretty good: A review of new descriptive methods in food science[END_REF]. In the following a brief overview on rapid descriptive methods is provided, which can basically be defined into three groups. The first group are verbal-based methods including free choice profiling [START_REF] Williams | The use of free-choice profiling for the evaluation of commercial ports[END_REF], flash profiling [START_REF] Dairou | A comparison of 14 jams characterized by conventional profile and a quick original method, the flash profile[END_REF] and check-all-that-apply questionnaires. From verbal-based methods a direct description of the products is obtained similar to QDA, but the time-consuming steps of attribute and scaling alignment of classical methods is avoided. The second group are reference-based methods, which include preselected reference sample. Polarised sensory positioning [START_REF] Teillet | Sensory methodologies and the taste of water[END_REF] and pivot profiling [START_REF] Thuillier | Pivot© profile: A new descriptive method based on free description[END_REF] are examples for this group. The third group consists of similarity based methods which focus on the overall assessment of the similarity of samples. The most important techniques belonging to the third group are sorting [START_REF] Lawless | Multidimensional scaling of sorting data applied to cheese perception[END_REF][START_REF] Schiffman | Introduction to multidimensional scaling: Theory, methods, and applications[END_REF], projective mapping [START_REF] Risvik | Projective mapping: A tool for sensory analysis and consumer research[END_REF] and its modification napping [START_REF] Pagès | Recueil direct de distances sensorielles: application à l'évaluation de dix vins blancs du Val-de-Loire[END_REF] on, which the main emphasis is laid in the following.

Projective mapping

During projective mapping, assessors are encouraged to position a set of samples on a sheet of paper according to perceived similarities. Samples which are perceived as similar are placed close to one another and samples which are perceived as different, are positioned away from one another. An fictitious example of a taster sheet is shown in Figure 2.4. X-and y-coordinates for each sample are collected and summarized in a table for each taster. Panellists can also be asked to describe each product by writing a few words (freely chosen) directly on the sheet near the products, which has been referred to as Ultra Flash Profiling [START_REF] Perrin | Comparison of three sensory methods for use with the Napping® procedure: Case of ten wines from Loire valley[END_REF]. Assessors are permitted to re-taste the samples as often as they want and to take as much time as needed.

The sensory attributes provided for each wine are collected, similar descriptors are usually grouped together, and the citation of each descriptor group is finally counted for each wine. In this manner a table of citation frequencies of each descriptor group for each sample is obtained. Usually not more than 10 to 15 samples can be evaluated depending on the product and how pronounced the differences among samples are.

The final structure of the data is displayed in Figure 2.5. The napping approach is a special way of performing projective mapping with a specified protocol regarding paper size, task instructions and data analysis method (MFA). Different modification of the napping approach have been reported [START_REF] Pagès | Collection and analysis of perceived product inter-distances using multiple factor analysis: Application to the study of 10 white wines from the Loire Valley[END_REF][START_REF] Perrin | Comparison of three sensory methods for use with the Napping® procedure: Case of ten wines from Loire valley[END_REF][START_REF] Perrin | Construction of a product space from the ustra-flash profiling method: application to 10 red wines from the loire valley[END_REF][START_REF] Pagès | The sorted napping: A new holistic approach in sensory evaluation[END_REF]. ... Coordinates of projective mapping scheets

X 1 X 2 X K D Figure 2
.5: Data structure of projective mapping with Ultra Flash Profiling. Tasting sheets of K assessors are represented as matrices X k which consist of the x-and ycoordinates of each sample. Citation frequencies of N descriptor groups from Ultra Flash Profiling are represented as matrix D.

Multiple factor analysis (MFA)

The analysis of projective mapping data is challenging due to the complexity of the data, especially when citation frequencies have to be included into the analysis. The main requirement for multivariate methods is finding a configuration which represents the consensus of the projective maps of all the panellists. To analyse sorting data usually Multidimensional Scaling (MDS) [START_REF] Lawless | Multidimensional scaling of sorting data applied to cheese perception[END_REF] and to a lesser extent DISTATIS [START_REF] Abdi | Analyzing assessors and products in sorting tasks: DISTATIS, theory and applications[END_REF] and Correspondence Analysis (CA) [START_REF] Bouteille | Sensory exploration of the freshness sensation in plain yoghurts and yoghurt-like products[END_REF] are used. For the analysis of data from projective mapping General Procrustes Analysis (GPA) [START_REF] Risvik | Projective mapping: A tool for sensory analysis and consumer research[END_REF] and INDSCAL [START_REF] Barcenas | Projective mapping in sensory analysis of ewes milk cheeses: A study on consumers and trained panel performance[END_REF]) have been reported. Multiple Factor Analysis (MFA), which was introduced with the napping approach, is another powerful multivariate method [START_REF] Pagès | Collection and analysis of perceived product inter-distances using multiple factor analysis: Application to the study of 10 white wines from the Loire Valley[END_REF].

Besides SUMPCA, consensus PCA, STATIS and multiblock correspondence analysis, MFA belongs to the family of multi-block or multi-table PCA methods. All these methods decompose a matrix X, consisting of the submatrices X k , which are normalised in different manners for each method. MFA can be computed as the PCA of the matrix X, with each submatrix X k weighted (scaled) by the inverse of its first singular value. The first step of MFA is therefore a PCA for K submatrices X k with M rows and N k columns via their SVD:

X k = U k S k (V k ) T , (2.8)
where U k is an orthogonal I × I-matrix, S k is a rectangular diagonal I × J k -matrix with non-negative entries and V k is an orthogonal J k ×J k -matrix. The I columns of U k and the N k columns of V k are the left singular vectors and the right singular vectors of

X k . The diagonal entries of S k are the so-called singular values σ 1,k ≥ • • • ≥ σ r,k > 0 of X k , where r = min{I, J k }.
The second step consists of the normalisation of all K submatrices X k with I rows and J k columns by the inverse of their first singular values σ 1,k and subsequent concatenation to the complete final I × J-matrix Z where J = J k .

Z = σ -1 1,k X 1 |σ -1 1,k X 2 | • • • |σ -1 1,k X K (2.9)
Each observation can be assigned a mass which reflects its importance. When all observations have the same importance, their masses are all equal to m i = 1 I . For reasons of simplicity masses are not taken into account here. A global PCA is finally obtained by Singular Value Decomposition (SVD) of Z:

Z = P S( Q) T , (2.10)
In PCA, equation 2.10 is rewritten as

Z = F Q T with F = P S (2.11)
where F is a I × I-matrix storing the factor scores (describing the samples/observations) and Q is J ×J-matrix storing the loadings (describing all variable submatrices).

The relationship of the PCAs of each submatrix with the global analysis can be explored by computing loadings (e.g. correlations) between the components of each submatrix and the components of the global analysis. For more details and examples on the calculation of MFA see [START_REF] Abdi | Multiple factor analysis: principal component analysis for multitable and multiblock data sets[END_REF]. [START_REF] Bro | PARAFAC. Tutorial and applications[END_REF][START_REF] Rodríguez | Determination of mycotoxins (aflatoxins and ochratoxin A) using fluorescence emission-excitation matrices and multivariate calibration[END_REF][START_REF] Behrends | A software complement to AMDIS for processing GC-MS metabolomic data[END_REF][START_REF] Tauler | Multivariate curve resolution applied to second order data[END_REF].

An alternative, more comprehensive approach aimed at the extraction of more information and underlying patterns in the data involves the use of the two dimensional raw data signal (GC-MS chromatogram) of each sample in entirety as a chromatographic fingerprint for modelling. Examples for holistic non-targeted analyses can be found in numerous reports [START_REF] Ballabio | Classification of GC-MS measurements of wines by combining data dimension reduction and variable selection techniques[END_REF]Sinkov and Harynuk, 2011;[START_REF] Daszykowski | No-alignment-strategies for exploring a set of two-way data tables obtained from capillary electrophoresis-mass spectrometry[END_REF][START_REF] Durante | A classification tool for N-way array based on SIMCA methodology[END_REF][START_REF] Cocchi | Threeway principal component analysis of the volatile fraction by HS-SPME/GC of aceto balsamico tradizionale of modena[END_REF][START_REF] Durante | Application of N-PLS to gas chromatographic and sensory data of traditional balsamic vinegars of Modena[END_REF]Christensen et al., 2005b,a;[START_REF] Christensen | Practical aspects of chemometrics for oil spill fingerprinting[END_REF][START_REF] Ferreira | Monitoring Alcoholic Fermentation: An Untargeted Approach[END_REF], some of which also include the application of multi-way analysis methods such as Tucker3, PARAFAC and Multi-way Partial Least Squares (N-PLS) to hyphenated chromatographic data. When factor models are used on chromatographic data, challenges are associated with the increased size of data and the handling of shifts and peak shape deformation among chromatograms, which result in distortion of the bilinear/trilinear structure of the data. Several algorithms and software programmes have been developed for peak alignment [START_REF] Nielsen | Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping[END_REF][START_REF] Skov | Automated alignment of chromatographic data[END_REF][START_REF] Tomasi | Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data[END_REF][START_REF] Lange | A geometric approach for the alignment of liquid chromatography -mass spectrometry data[END_REF]Sinkov et al., 2011). Depending on the data, shift correction can, however, be difficult and time-consuming.

The above described problems of conventional data analysis approaches to nontargeted GC-MS analysis, in particular challenges with automated peak integration and retention time alignment of chromatograms, were the main motivation for the development of an alternative data analysis approach. The course of the realization and implementation of ideas is described during this chapter. The major consideration to overcome the peak integration issue was the direct modelling of the chromatographic raw data (without feature selection), including a reduction of the data. The main idea to master the distortion of the data structure due to shifting peaks was the use of a mathematical transformation of pieces (segments) of the chromatograms using SSCP matrices. SSCP matrices are positive, squared and symmetric, simi-lar to variance-covariance matrices [START_REF] Lay | Linear Algebra and Its Applications[END_REF], which are utilised for instance in PARAFAC2, STATIS and the calculation of R V -coefficients [START_REF] Danielsson | Rapid multivariate analysis of LC/GC/CE data (single or multiple channel detection) without prior peak alignment[END_REF][START_REF] Daszykowski | No-alignment-strategies for exploring a set of two-way data tables obtained from capillary electrophoresis-mass spectrometry[END_REF][START_REF] Daszykowski | Methods for the exploratory analysis of two-dimensional chromatographic signals[END_REF][START_REF] Stanimirova | STATIS, a three-way method for data analysis. Application to environmental data[END_REF][START_REF] Bro | PARAFAC2 -Part II. Modeling chromatographic data with retention time shifts[END_REF]. Particularly the indirect fitting algorithm for PARAFAC2 (Harshman, 1972) served as a major inspiration for the development of the new approaches.

Moreover, for the sake of simplicity another aim was to use a single model for the entire set of chromatograms of all samples to find systematic differences among samples and to identify important regions of the chromatograms which, if desired, can be further deconvoluted and investigated using e.g. PARAFAC2 or AMDIS. A method using multiple PARAFAC2 models on segmented chromatograms has been reported recently (Amigo et al., 2010a). This approach gives very detailed information on fully decomposed mass spectra and peak profiles, which are finally summarized using PCA. The here described new approach can be considered as a 'segment pre-selection tool' for subsequent deconvolution of only important chromatogram segments. By this means a significant amount of time used for the deconvolution of chromatogram segments (e.g. construction and evaluation of PARAFAC2 models) can be save.

This chapter gives an overview on the algorithms of the new data analysis approaches, including the theoretical background on mathematical transformations such as the calculation of SSCP matrices and SVD. The approaches are explained and tested on an artificial, well defined GC-MS data set with and without peak shifts.

Moreover, the limitations of the established methods such as PCA and Tucker3 on the artificial GC-MS raw data in terms of variable size and peak shifts are discussed.

After the theoretical discussion, the approaches are demonstrated on a real GC-MS dataset of experimental wines and results are confirmed using a reference method including PARAFAC2 deconvolution and peak integration of deconvoluted peak profiles of the entire segmented chromatograms with subsequent PCA on the obtained peak table.

Defined, artificial GC-MS data set

To demonstrate and verify the developed algorithms a defined, artificial GC-MS data set was created using an in-house developed MATLAB script. The data set consists of 20 chromatograms, each containing 9 to 10 gaussian peaks with different mass spectra (35 u to 318 u) and different degrees of overlapping. The whole chromatogram can be divided into five segments. Segment one contains two peaks which perfectly overlap. Peaks three and four partially coelute in segment two, which is also the case for the peaks five, six and seven in segment three. Peak eight is in segment four and the last segment contains the last two peaks nine and ten, which also partially coelute (Figure 3.1 and 3.2). Peak sizes of three peaks vary among chromatograms as indicated in Table 3.1, consequently samples can be divided into four groups. Moreover, a small random variation was added to all peak sizes to simulate a natural deviation of measurements.

Limitations of PCA and Tucker3 on chromatographic raw data

Feature selection such as automated integration of peaks is not needed, when multivariate models are used directly on chromatographic raw data. A large number of variables and shifting retention time profiles pose problems for multivariate models in terms of the distortion of the bilinear/trilinear structure of the data and in terms of reasonable stability and reliability of multivariate analyses, respectively.

Artificial GC-MS data without peak shifts

To demonstrate the above mentioned issues PCA was applied to the TIC of all samples of the artificial GC-MS data set without peak shifts as well as on the entire unfolded three-way array which was rearrange in a way that the mass spectral dimension was eliminated as indicated in Figure 3.3. Furthermore, taking the multiway nature of the artificial data set into account Tucker3 was used to decompose the three-way array. For preprocessing auto-scaling and mean-centering was used. If variables are not scaled to unit variance prior to PCA, all variables with the highest variance or standard deviation, respectively, will have the biggest influence on the model. In other words larger variables will evidently have a larger influence on the model than smaller variables. Although samples grouped very well together in the here presented example, it might not be a good idea to use only mean-centering in a real world situation, as small, but important variables (or peaks), could easily be missed. Moreover, in the here presented example the samples 14 and 15 which contrary to the other samples contain peak number two can not be separated. As the TICs are the sum of all mass channels, the information on the relative small peak number 2 in the samples 14 and 15 is simply lost during the summation of all masses. It is possible to prevent the loss of the information of the mass dimension by unfolding the three-way array prior to PCA as shown in Figure 3.3. The unfolding of the array (i × j × k ), with i number of samples, j scans (the elution profile) and k mass channels results in a new matrix (i × jk ). In the here presented example this matrix is of size 20 × 311300 (20 × (1100 × 283)). In fact this matrix is much bigger than the matrix of TICs, which worsen the issue of an excessive number of variables for PCA modelling in terms of reasonable stability and reliability of the model. The Figures 3.6 and 3.7 show the scores and loadings plots of the first two components of the PCAs on the unfolded three-way array with auto-scaling and with mean-centering only, respectively. The first two principal components of the PCA on the auto-scaled data (Figure 3.6) explain a very low amount of 6.3 % and 5.9 % of the total variance in the data set. The first two principal components of the PCA on the mean-centered data (Figure 3.7) explain 84.2 % and 15.7 % of the variance in the data.

I J K I JK K 2K 1 ...
All other PCs do not explain any structural information. The results are very similar to the above discussed PCAs on the TICs. Although, the mass dimension remained intact when the three-way array was unfolded, the differences of the samples 14 and 15 which are the only samples containing the relative small peak number two are not well reproduced in the two PCAs. Without scaling to unit variance all variables with small variances have very little influence on the PCA. Scaling to unit variance, however, extremely up weight noise, as stated above already. 3) of the artificial data set (without peak shifts). Samples are coloured according to Table 3.1.

unfolded mode, so that the effect of one variable is associated with more than one element of a loading vector. A two-way PCA model can therefore be considered to This example shows that Tucker3 can be considered to be the simpler model in a multi-way context. Moreover, it is apparent that this multi-way model is much easier to interpret.

A [3 3 3]-Tucker3 model on the artificial GC-MS dataset which was mean-centered across the first mode (samples) and scaled to unit variance within the third mode (mass channels) was constructed. In order to simplify the interpretation of the model the initial Tucker3 core was rotated to optimal diagonality (Table 3. 3.2: Eight largest core entries and their corresponding explained variation (sum of squares) of the [3 3 3]-TUCKER model on the three-way array of the artificial GC-MS data set (sorted in descending order).

Artificial GC-MS data with peak shifts

In the previous section all models have been tested on a data set which did not contain any shifts in the retention profile among samples. Nevertheless, experimental chromatographic data most often contain shifts among samples. Before factor models can be applied directly on real chromatographic data peak alignment is inevitable. The possibility of the usage of multi-way models such as Tucker3 to decompose multi-way chromatographic data such as GC-MS chromatograms from multiple samples has been demonstrated in the previous chapter. Feature selection such as auto-mated peak integration which, dependent on the data, can be troublesome can so be avoided. Peak shifts and a serious problem when factor models are directly applied to chromatographic raw data as has been demonstrated in the last example (Figure 3.9). All in all, this issue shows the necessity for further development of data analysis approaches which take shifting peaks into account. 3.1.

Approach 1: 'chromatogram segmentation, SSCP matrices and PARAFAC'

The in this and the following section discussed development of two new approaches to GC-MS data analysis were primarily inspired by the indirect fitting algorithm for PARAFAC2 [START_REF] Harshman | PARAFAC2: Mathematical and technical notes[END_REF], in which SSCP matrices are used to compensate for the distortion of the trilinearity of three-way data. The aim was the usage of a single model for the whole chromatograms of all samples to obtain information on systematic differences among samples. Out of the principal idea of the indirect fitting algorithm for PARAFAC2 a new idea was developed that makes the modelling of the entire chromatograms of all samples possible by implementing segmentation and mathematical transformation of chromatogram segments of each sample into SSCP matrices. In this manner the new approaches cope without peak integration and peak alignment.

In the following the basic ideas and the development of a first approach are discussed. This first approach includes segmentation of chromatograms, mathematical transformation of chromatogram segments using SSCP matrices and PARAFAC modelling of the obtained three-way array of the transformed chromatographic raw data.

Theoretical background

Using basic matrix algebra a SSCP matrix XX T is obtained by multiplication of a matrix X with its transpose, as displayed in Equation 3.1.

XX T =          C j=1 x 2 1j C j=1 x 1j x 2j • • • C j=1 x 1j x Rj C j=1 x 2j x 1j C j=1 x 2 2j • • • C j=1 x 2j x Rj . . . . . . . . . . . . C j=1 x Rj x 1j C j=1 x Rj x 2j • • • C j=1 x 2 Rj          , (3.1)
where X is a R × C-matrix of elements x ij , i = 1, . . . , R, j = 1, . . . , C. The matrix product XX T is the R × R matrix of Sums of Squares and Cross Products (SSCP matrix). In Detail, the diagonal of XX T includes the sums of squares with respect to a given row i of X, namely C j=1 x 2 ij . Moreover, all off-diagonal elements represent cross products between two different rows i, k of X, in particular C j=1 x ij x kj for i = k. Consequently, the sums of squares are a measure of variation within a row, whereas the cross products are a measure of covariation between two rows. Note the similarity to the variance-covariance matrix: diagonal elements of the variancecovariance matrix are variances and all off-diagonal elements are covariances1 . The terms variation and variance as well as covariation and covariance can for the sake of simplicity be replaced in the following (although not strictly mathematically true).

PARAFAC2 is a powerful tool for the deconvolution of small chromatogram segments [START_REF] Bro | PARAFAC2 -Part II. Modeling chromatographic data with retention time shifts[END_REF]Amigo et al., 2010a[START_REF] Amigo | Solving GC-MS problems with parafac2[END_REF][START_REF] Johnsen | Automated resolution of overlapping peaks in chromatographic data[END_REF]. The approach presented here is mainly inspired by the idea of the indirect fitting algorithm of the PARAFAC2 model, which instead of modelling an array consisting of the matrices X i (spectral profile × elution profile for I samples) directly considers a model of an array consisting of the SSCP matrices X i (X i ) T [START_REF] Bro | Multi-way analysis in the food industry: models, algorithms, and applications[END_REF][START_REF] Harshman | PARAFAC2: Mathematical and technical notes[END_REF]. In this manner, PARAFAC2 is suitable for deconvoluting chromatographic peaks shifting along the retention axis among samples. A disadvantage of PARAFAC2 is that for each segment of the chromatogram a single model has to be constructed and evaluated. Figure 3.10 shows an visualised example of three identical but shifted two dimensional GC-MS peaks (simulated data), represented as the matrices X, Y and Z, and their SSCP matrices XX T , Y Y T and ZZ T . The table in 3.10(a) elucidates that the three SSCP matrices are constant.

The utilisation of SSCP matrices as a preprocessing step for multivariate mod-Raw signal data points SSCP matrices 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 4 6 8 10 12 10 8 6 4 2 0 0 0 0 0 0 0 0 0 0 584 728 584 X 0 2 4 6 8 10 12 14 12 10 8 6 4 2 0 0 0 0 0 0 0 0 XX T 0 728 924 728 0 0 2 4 6 8 10 12 10 8 6 4 2 0 0 0 0 0 0 0 0 0 0 584 728 584 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 4 6 8 10 12 10 8 6 4 2 0 0 0 0 0 0 584 728 584 Y 0 0 0 0 0 2 4 6 8 10 12 14 12 10 8 6 4 2 0 0 0 0 Y Y T 0 728 924 728 0 0 0 0 0 0 2 4 6 8 10 12 10 8 6 4 2 0 0 0 0 0 0 584 728 584 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 4 6 8 10 12 10 8 6 4 2 0 0 0 584 728 584 Z 0 0 0 0 0 0 0 0 2 4 6 8 10 12 14 12 10 8 6 4 2 0 ZZ T 0 728 924 728 0 0 0 0 0 0 0 0 0 2 4 6 8 10 12 10 8 6 4 2 0 0 0 584 728 584 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 elling of whole chromatograms has been reported before [START_REF] Daszykowski | No-alignment-strategies for exploring a set of two-way data tables obtained from capillary electrophoresis-mass spectrometry[END_REF][START_REF] Daszykowski | Methods for the exploratory analysis of two-dimensional chromatographic signals[END_REF]. If entire two dimensional chromatograms are used for the construction of SSCP matrices, information on the retention time of compounds is lost, complicating the identification of peaks contributing to the differentiation among samples.

However, by dividing all chromatograms along the retention axis into segments containing a small number of peaks and subsequent construction of SSCP matrices for each segment, information on the location of peaks in the chromatogram contributing to the differentiation of samples can be preserved. In the here presented new approach 1, the SSCP matrices for each segment and each sample have dimensions The whole procedure is summarized in matrix notation in the following. Each two dimensional chromatogram (sample) is characterized by M mass channels and N scan points. N is divided into K segments, that is N = K k=1 N k , where N k describes the number of scans in the k-th segment. In particular, altogether we have I samples.

First, we define an I × K-matrix X by

X = (X ik ) i=1,...,I k=1,...,K =       X 11 • • • X 1K . . . . . . . . . X I1 • • • X IK       , (3.2)
where X ik is a M × N K -matrix containing the data of the i-th sample and k-th segment, that is

X ik = (x ik mn ) m=1,...,M n=1,...,N k =       x ik 11 • • • x ik 1N k . . . . . . . . . x ik M 1 • • • x ik M N k       . (3.
3)

The SSCP matrix A ik = X ik (X ik ) T containing information on the variation and covariation between all mass channels of the i-th sample and k-th segment is defined by

A ik = (a ik rt ) r,t=1,...,M (3.4)
with

a ik rt = N k s=1 x ik rs x ik st ∀r, t = 1, . . . , M (3.5)
and dim

(A ik ) = M × M, (3.6)
for all i = 1, . . . , I and k = 1, . . . , K.

Subsequently only the upper triangular part of the symetric SSCP matrix A ik is vectorised (unfolded) and concatenated into a new matrix Y k (compilation matrices).

The vectorisation vec(A ik ) of the upper triangular of A ik is defined by

2 vec(A ik ) = α ik 1 α ik 2 • • • α ik M , (3.7)
where

α ik l = (a ik l,l , a ik l,(l+1) , . . . , a ik l,M ) ∀l = 1, . . . , M, (3.8)
for all i = 1, . . . , I and k = 1, . . . , K.

Consequently, the vectorisation vec(A ik ) has

J = M l=1 l = M (M +1) 2
components.

The I × J-matrix Y k is constructed by the above row vectors vec(A 1k ), . . . , vec(A Ik ) as follows:

Y k =       vec(A 1k ) . . . vec(A Ik )       , (3.9)
for all k = 1, . . . , K.

In the end, we form SSCP matrices Z k = Y k (Y k ) T , which contain information on the variation and covariation between all samples in the k-th segment with regard to the variation and the covariation between all mass channels of the i-th sample and k-th segment,

Z k = (Z k rs ) r,s=1,...,I (3.10) with Z k rs = vec(A rk ) • (vec(A sk )) T ∀r, s = 1, . . . , I, (3.11)
for all k = 1, . . . , K. Finally, the matrices Z k are rearranged into the (I ×I)×K-array

Z: Z = Z 1 • • • Z K . (3.12)
Prior to multi-way analysis the three-way array Z is mean centered across the first and second mode and scaled to unit variance within the third mode. The term mode refers here to the dimension of the array.

Application of approach 1 to the artificial GC-MS data set

The artificial GC-MS data set was analysed using the new approach to show its validity. To prove theoretical considerations the new approach was first tested on the artificial GC-MS data set without noise and without peak shift. Subsequently, the new approach was tested on the artificial GC-MS data set with noise and non-linear peak shifts to show that the new algorithm can compensate peak shifts.

In the artificial GC-MS data set each of the three differences among groups of samples (see Table 3.1) is caused by varying peak sizes in different segments. After segmentation and mathematical transformation the resulting three-way array contains information on the covariation among samples in terms of differences in their mass traces in each segment. The decomposition of this array using PARAFAC is therefore expected to give one component to explain each of the three differences among the four groups of samples. Noise was excluded from the artificial data set, as it is a source of random variation.

In fact, after applying approach 1 a three component PARAFAC model fully component. Furthermore, component four explaining 3.5 % variation reflected unsystematic variation in the data (Figure 3.12(c)), which is related to noise. Note that a PARAFAC model on the shifted artificial GC-MS data set which does not contain noise results in a three component model (model not shown). Overall, the same structural information on the differences among samples could be extracted from the artificial GC-MS data set with and without peak shifts using the developed approach.

The three-way data array which is obtained after the segmentation and mathematical transformation can also be seen as a 'stack' of matrices. It seems therefore reasonable to evaluate different multi-block methods for the analysis of this data type besides multi-way methods. Different multi-block methods have been applied to the three-way array, in such a manner such that each slab of the array corresponds to a segment. The following methods were tested: PCA on concatenated matrices, MFA [START_REF] Escofier | Analyses factorielles simples et multiples: objectifs, méthodes et interprétation[END_REF], Common Components and Specific Weights Analysis (CCSWA) [START_REF] Mazerolles | Common components and specific weights analysis: a chemometric method for dealing with complexity of food products[END_REF], analysis of co-inerita with common components [START_REF] Chessel | Analyses de la co-inertie de K nuages de points[END_REF] and STATIS [START_REF] Stanimirova | STATIS, a three-way method for data analysis. Application to environmental data[END_REF] using the SAISIR toolbox for MATLAB [START_REF] Cordella | SAISIR: a new general chemometric toolbox[END_REF] (d) Saliences: q 1 to q 4 Figure 3.13: Scores and saliences (weights of blocks/segments) of CCSWA on the three-way array of the segmented and mathematically transformed artificial GC-MS dataset with shifted peaks and noise. Samples are coloured according to Table 3.1.

Approach 2: 'SVD on each segment and PCA on eigenvalues'

The second approach was mainly inspired by the first one. The idea of segmenting chromatograms was kept, but the mathematical transformation of segments was changed. Each segment for each sample is decomposed using singular value decomposition (SVD) and only the first few singular values of each SVD are kept for further multivariate modelling using PCA.

Theoretical background

The basic idea behind SVD is the reduction of high dimensional data, such as large matrices with many variables, to a lower dimensional space which compromises the substructure of the data. SVD transforms correlated variables into fewer uncorrelated variables showing the various relationships among the original subjects (samples). In this way dimensions explaining most of the variation in the data are obtained. SVD can therefore also be understood as a data reduction method.

Many applications in signal processing and statistics make use of SVD. SVD is, for instance, the most often used algorithm for PCA. SVD consists of finding the eigenvalues and eigenvectors of the SSCP matrices XX T and X T X to obtain the left and right singular vectors (U an V ), respectively, and the singular values in the diagonal of S, which are the square roots of the eigenvalues from XX T or X T X. Eigenvectors and eigenvalues exist in pairs meaning every eigenvector has a corresponding eigenvalue. Eigenvectors are new directions in the original data cloud, eigenvalues reflect the variance in the data in that direction. Singular values are becoming less important with descending indices. The first direction (component) explains therefore most of the variance in the data, the second direction the second most variance in the data and so on [START_REF] Salkind | Encyclopedia of measurement and statistics[END_REF]. As SSCP matrices are used for the singular value decomposition the same assumption on retention time shifts among samples hold as for approach 1 when SVD is applied to chromatographic segments of samples.

Approach 2 can be summarized as follows: After segmentation of the chromatograms (analogue to approach 1) each segment for each sample is decomposed using SVD, while only the first few singular values of each decomposition are used for further data analysis. The number of singular values to keep depends on the number of peaks in the segments (rank of the matrices). Note that for the sake of simplicity the segment size should be kept small similar to approach 1. The more similar segments are among samples the more similar are their decompositions. For instance replicates of samples show the same (or very similar) decomposition patterns, and have therefore the same (or very similar) singular values. For each sample all singular values of all samples are simply concatenated. In this way a matrix is obtained which after class centroid centering and scaling to intra-class variance can be analysed with PCA. A discussion on preprocessing of this matrix is presented in the next section.

The approach is summarized in matrix notation in the following. The segmentation of chromatograms is carried out according to equations 3.2 to 3.3 from approach 1.

The SVD of X ik is defined as follows:

X ik = U ik S ik (V ik ) T , (3.13)
where U ik is an orthogonal M × M matrix, S ik is a rectangular diagonal M × N k matrix with non-negative entries and V ik is an orthogonal N k × N k -matrix. The M columns of U ik and the N k columns of V ik are the left singular vectors and the right singular vectors of X ik . The diagonal entries of S ik are the so-called singular values

σ ik 1 ≥ • • • ≥ σ ik r > 0 of X ik , where r = min{M, N k }.
Singular values are becoming less important with descending indices, we therefore only take the first Q singular values into consideration and represent them as a row vector, that is

s ik = (σ ik 1 , . . . , σ ik Q ), (3.14)
for all i = 1, . . . , I and k = 1, . . . , K. Moreover, the row vectors s ik are concatenated3 to a row vector s i over all K segments,

s i = s i1 s i2 . . . s iK , (3.15)
for all i = 1, . . . , I. Finally, we form the (QK) × I-matrix Z by means of all row vectors s 1 , . . . , s I as follows:

Z =       s 1 . . . s I       . (3.16)
The final matrix Z is class centroid centred and scaled to intra-class variance before conducting PCA.

Application of approach 2 to the artificial GC-MS data set

The performance of approach 2 on the artificial GC-MS data set without retention time shift and without baseline noise is shown in the following. The small random variation of peak sizes was however included to simulate a natural deviation of mea- From the scores some structure among the samples can be observed, but no clear differentiation between all groups of samples is apparent. The same holds when only two singular values per segment are kept (data not shown). When class centroid centering and scaling to intra-class variance is applied for preprocessing of PCA clear separation between the groups of samples is obtained (Figure 3.15). Principal component 1 (99.9 % explained variance) reflects the differences of sample 14 and 15, which solely contain peak 2 (segment 1). From the loadings plot (Figure 3.15(b)) it is evident that the second and third singular value of segment 1 are responsible for this difference. The difference of peak 9 in segment 5 between the first ten and the last ten samples is explained by PC2 (0.1 % explained variance). Accordingly, the fist two singular values of segment 5 show high loadings on PC2. Principal component 3 (Figure 3.16) explaining 0.1 % of variance reveals differences in segment 2 (peak 4) between samples. 3.1. Numbers in the loadings plots refer to the segment and the singular value of the segment (e.g. 1 2: segment 1, second singular value).

The application of approach 2 on the artificial data set with and without noise and peak shifts resulted in similar groupings between samples. Results from PCA with class centroid centering and scaling to intra-class variance for the data set with noise and peak shifts are shown in Figure 3.17 and 3.18. In brief, PC1 reflects the differences of samples 14 and 15, PC2 shows the differences between the first ten and the last ten samples (Figure 3.17(a)). The difference of the samples one to five is explained by PC2 and PC3 (Figure 3.18(a)).

Approach 2 gives similar results to approach 1. However, approach 1 seems to be less sensitive to peak shifts than approach 2, as the variation inside the four 3.1. Numbers in the loadings plots refer to the segment and the singular value of the segment (e.g. 1 2: segment 1, second singular value). groups of samples is smaller for approach 1 (Figures 3.12(a) and 3.12(a)) compared to approach 2 (Figures 3.17(a) and 3.18(a)). Moreover, approach 1 is unsupervised while for the class centroid centering and scaling to intra-class variance, the preprocessing of the PCA of approach 2 is supervised. Yet can approach 2 be seen as the 'simpler' approach, as the final PCA of approach 2 is easier, still provides interpretable results and is quicker to model compared to the PARAFAC model of approach 1. 3.1. Numbers in the loadings plots refer to the segment and the singular value of the segment (e.g. 1 2: segment 1, second singular value).

Application of the new data analysis approaches to experimental GC-MS data

The in Section 3.4 and 3.5 presented approaches are in the following tested on a set of real HS-SPME-GC-MS chromatograms of experimental wines.

Experimental

The data set explored in this study consists of solid phase microextraction (SPME) GC-MS analysis of Cabernet Sauvignon wines, which were fermented with different combinations of yeast and lactic acid bacteria using sequential inoculation and coinoculation strategies.

Wine Samples

All wines were produced from the same Cabernet Sauvignon grapes from the 2012 vintage. Fermentations were carried out using six combinations of yeast and lactic acid bacteria commonly used in the wine industry to study their influence on the volatile composition of wines comparatively. Three wines were made with the yeast Lalvin Clos and the lactic acid bacteria Enoferm Alpha, Enoferm Beta and Lalvin PN4; two wines were made with the yeast Uvaferm RBS and the lactic acid bacteria Lalvin VP41 and O-Mega; and one wine was made with the yeast Uvaferm VRB and the lactic acid bacteria Enoferm Alpha (all from Lallemand Inc., Canada).

MLF is commonly conducted after alcoholic fermentation. However, alcoholic and malolactic fermentation can also be done simultaneously to save time and to prevent the risk of spoilage of the wine between the two fermentations. For this purpose, lactic acid bacteria are usually inoculated 24 h after yeast inoculation to conduct a simultaneous alcoholic and malolactic fermentation. This mode of inoculation is also called co-inoculation.

To obtain information on the differences of these two modes of inoculation all of the six yeast/bacteria combinations were fermented with sequential and co-inoculation of yeast and lactic acid bacteria. In total, the volatile composition of 12 experimental wines was studied here (Table 3.3). All yeast/bacteria combinations are commonly used in the wine industry. The major aim was to obtain analytical data of their impact on the volatile composition of wine.

HS-SPME-GC-MS Analysis

Headspace solid phase microextraction (HS-SPME) sampling was carried out in randomized order using a 100 µm polydimethylsiloxane (PDMS) fibre and the following procedure: 5 mL of the wine sample was transferred to a 20 mL headspace crimp-top vial and spiked with 152 µg L -1 ethyl hexanoate-d11 as internal standard.

Two grams of sodium chloride (preheated to 250 • C and cooled to room temperature)

were added and the vial was capped immediately using a PTFE-lined septum and aluminium cap. Each wine sample was submitted to HS-SPME sampling with agi-tation at 500 rpm for 30 min. Fiber blank and column blank analyses were carried out regularly to confirm that no sample carry-over occurred. A standard 12 % hydroalcoholic solution containing some esters and alcohols commonly present in wine was regularly analysed to monitor the performance of the system. Works Inc., Natick, MA, USA) using built-in functions. All further data processing was done in MATLAB utilizing the freely available N-way toolbox [START_REF] Andersson | The N-way Toolbox for MATLAB[END_REF] and in-house written functions. Each of the 36 GC-MS raw chromatograms was transformed into a matrix of size 3977 × 266 (elution profile × spectral profile).

Deconvoluted mass spectra were exported as ASCII text files in NIST .msp format using an in-house written MATLAB function and imported into NIST 08 spectral library [START_REF] Stein | NIST Mass Spectral Search Program[END_REF].

Application of approach 1 to experimental GC-MS data

The developed fingerprinting approach were applied to GC-MS data obtained for a set of twelve Carbernet Sauvignon wines fermented with different yeast/bacteria combinations using co-inoculation and sequential inoculation to study the impact of these factors on the volatile composition of the wines. SPME was chosen for sample preparation because of its simplicity for wine analysis in terms of full automation, speed and sensitivity [START_REF] Vestner | Investigation of the volatile composition of pinotage wines fermented with different malolactic starter cultures using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOF-MS)[END_REF][START_REF] Rocha | Headspace solid phase microextraction (SPME) analysis of flavor compounds in wines. Effect of the matrix volatile composition in the relative response factors in a wine model[END_REF][START_REF] Antalick | Development, validation and application of a specific method for the quantitative determination of wine esters by headspace-solid-phase microextraction-gas chromatography-mass spectrometry[END_REF].

Although SPME fibres with mixed phases allow the extraction of a wider range of compounds, a PDMS fibre was chosen, as all PDMS degradation products contain silicone, which facilitates the differentiation of analytes from artefacts by means of siloxane fragments present in the mass spectra of the latter. This is particularly important when performing non-targeted analysis. A fast temperature ramp was used in this study to provide relatively fast GC separation. Under these conditions some resolution is sacrificed. However, the data analysis approach reported here takes the entire mass dimension into account, and therefore complete separation of peaks is not needed provided that co-eluting compounds differ in terms of their mass spectra. During the analyse of all samples, the system stability was monitored using a hydro-alcoholic standard solution containing common wine volatiles including ethyl butanoate until ethyl decanoate, butanol until decanol, isoamyl alcohol, isoamyl acetate, citronellol and nerolidol. Reproducibility of analyses were ensured using these monitoring injections. Matrix effects on the SPME extraction were not expected, as the composition of the analysed wines were very similar. Moreover, no significant changes of the absolute peak areas of the internal standard among samples have been observed (T-test, α = 0.05 and n = 4 injections at beginning and end of the sequence). The added internal standard was therefore not used to correct chromatograms. It should generally be noted that depending on the phase, analytes and matrix certain effects may occur during the SPME procedure such as analytematrix and analyte-sorbent interaction. For instance a direct consequence of coating saturation is inter-analyte competitive adsorption. SPME remains, however, a very powerful sample preparation technique for non-targeted analysis, but the SPME procedure has to be considered carefully regarding for instance matrix differences among samples [START_REF] Souza-Silva | A critical review of the state of the art of solid-phase microextraction of complex matrices I. Environmental analysis[END_REF][START_REF] Gionfriddo | Headspace versus Direct Immersion Solid Phase Microextraction in Complex Matrixes: Investigation of Analyte Behavior in Multicomponent Mixtures[END_REF].

Initially, all chromatograms were divided into 84 small segments based on visual examination of overlays of total ion chromatograms (TICs) of all samples and of overlays of all mass channels for a single sample. Special attention was paid to avoid the inclusion of too many peaks in one segment and splitting of peaks into different segments. The latter is particularly important for segments containing peaks which shift between different samples. In this way, as few as possible peaks were included in each segment (one to five) and the dimensions of the segments ranged between 22 and 114 scans. The segments 15, 58 -62, 72, 76, 77, 80, 81, 83 were excluded from the data set as they either contained only baseline or artefacts in the chromatograms.

Seventy one small segments in total were kept for further analysis. To evaluate the effect of the number of segments, every two and every four neighbouring segments were combined which resulted in 36 and 18 bigger segments, respectively.

The outcome of the mathematical transformation (see section 3.4.1) of the segmented chromatographic raw data is a three-way array of size 36 × 36 × 71 (samples × samples × number of segments), 36 × 36 × 36 and 36 × 36 × 18, respectively.

All arrays were mean centered across the first and second mode and scaled to unit variance within the third mode. The array which was obtained from the smallest segments (total of 71 segments) was analysed using CCSWA, Tucker3 and PARAFAC. CCSWA did not show any interpretable results against expectation (not shown). Tucker3 re-sults were promising, although due to the nature of the Tucker3 model difficult to interpret. The results of the PARAFAC model were, however, much more informative and easier to interpret than the TUCKER3 results and revealed information on systematic differences among samples. The two other three-way arrays with 36 and 18 segments were therefore only analysed using PARAFAC. The number of components of the PARAFAC models were determined using the core consistency diagnostic [START_REF] Bro | A new efficient method for determining the number of components in PARAFAC models[END_REF], by examination of residuals, and by evaluating captured variance and number of iterations until the PARAFAC algorithm converged for models with one to 20 components. For the three-way array with 71 segments a eleven component PARAFAC model was chosen, explaining 73.0 % of the total variation in the data set.

The best PARAFAC models for the three-way array with 36 and 18 segments were a ten component PARAFAC model explaining 83.0 % of the total variation and a nine component PARAFAC model explaining 92.1 % of the total variation, respectively.

In general, PARAFAC loadings can be interpreted in the same way as PCA scores and loadings. In multi-way terminology, however, only the word 'loading' is used.

For each mode of the analysed multi-way array a loading matrix is obtained. In the approach presented here, the first and second modes of the obtained PARAFAC model are identical, as the SSCP matrices from equation 3.11, which were compiled into a three-way array in equation 3.12, are symmetric. Congruence loadings were calculated for the third mode (segment mode) and each segment with a congruence loading value higher than 0.5 was considered as a 'moderate to strong correlated' with the raw data. Depending on the aim of the study, this value can also be chosen lower (e.g. 0.3, 'weak correlation') or higher (e.g. 0.7 'strong correlation'). A higher value for instance would be suitable if only highly correlated segments are of interest.

The information content of the three PARAFAC models are discussed and compared in the following. the variation between wines fermented with different yeasts. Wines fermented with the yeast Uvaferm RBS (rbs) are separated from the wines fermented with the yeast Lalvin Clos (clos) and Uvaferm VRB (vrb) on component three (7.8 % explained variation), whereas the wines fermented with the yeast Uvaferm VRB differ from the other wines by component eleven (2.3 % explained variation). The impact of each segment on component three and eleven, respectively, is shown in the congruence loadings plots of the segment mode of these components in Figure 3.19(b). For component eleven only the segments 9 and 20 are responsible for the differences of the wines made with the yeast Uvaferm VRB compared to the wines made with the other two yeast starter cultures, considering congruence loading values higher than 0.5. The differences between the wines fermented with the yeast starter culture Uvaferm RBS and all other wines described by component three are correlated with the segments 1, 4, 8, 11, 14, 22, 23, 24, 30, 31 and 38. Segments 4,6,11,18,28,31,33,35,36,38,41,45,46,48,49,50,53,67, 74 and 75 had congruence loading higher than 0.5 on component one, while on component two segments 28,64,65,68,69,71,78 are important.

The relationship between the chromatographic raw data and the PARAFAC loadings of component two can be obtained by comparing the loadings in Figure 3 with the TIC-overlays of all injections in Figure 3.23. The TIC overlays in Figure 3.23 confirm that the segments 64, 68, 69 and 71 (highest congruence loadings on PARAFAC component two; Figure 3.20) contain information on unique differences between the wine sequentially fermented with Lalvin Clos and Enoferm Beta (clos beta seq) and all other wines. Component 4 explaining 6.9 % of the total variation in the data set differentiates the wine fermented with the yeast Lalvin Clos and the lactic acid bacteria Lalvin PN4 co-inoculated (clos PN4) from the other wines (Figure 3.21(a)). Responsible for this differences is especially segment 43, but also 41, 51 and 63 as shown in the congruence loading plot of the segment mode of this component (Figure 3.21(b)).

The results of the PARAFAC model with only 36 segments (neighbouring segments were combined) are very similar to the results of the PARAFAC model with 71 segments and will be discussed in the following. Component one of both PARAFAC models (Figure 3.20 and 3.24) reflect the same information, which is the differences of the wine fermented with the yeast Uvaferm RBS and the lactic acid bacteria O-Mega sequentially inoculated (rbs 271), and the difference between co-inoculated and In conclusion, the comparison of the results of the three PARAFAC models with different segment sizes shows that the size of the segments clearly has an influence on the information obtained from the PARAFAC model. While the models with small and medium size (71 and 36 segments respectively) revealed the same information on systematic differences in the data, important information on systematic differences among the wines caused by the different yeast starter cultures could not be obtained from the PARAFAC model with the biggest segments (18 segments). These results demonstrate that smaller segments are beneficial. Another positive aspect of smaller segments is that they are easier to deconvolute afterwards.

Application of approach 2 to experimental GC-MS data

To test approach 2 on the experimental data set the same segmentation as for the testing of approach 1 was used (see Section 3.6.2). Classes for class centroid centering and scaling to intra-class variance prior to PCA were first defined regarding the twelve treatments and subsequently regarding the three different yeast starter cultures used.

Initially, five different PCA models were tested for each scaling, where one to five singular values for each segment were kept. With the twelve treatments defined as groups the model with two singular values kept per segment revealed more information on the grouping of the samples compared to the first model (only one singular value per segment). The remainder of the models (more than 2 singular values kept per segment) did not reveal any extra information. For the models where classes were defined according to the three yeast starter cultures it was sufficient to keep only the first singular value of each segment. The PCA where classes for the preprocessing were defined according to the three yeast starter cultures is discussed in the following. PC1 and PC2 explain 40.8 % and 14.8 % of variance in the data, respectively (Figure 3.32). PC1 separates the wines fermented with the yeast Uvaferm RBS from all other wines. PC2 separates the wines fermented with the yeasts Uvaferm VRB and Lalvin Clos. Moreover, PC2

1_1 1_2 2_1 2_2 3_1 3_2 4_1 4_2 5_1 5_2 6_1 6_2 7_1 7_2 8_1 8_2 9_1 9_2 10_1 10_2 11_1 11_2 12_1 12_2 13_1 13_2 14_1 14_2 16_1 16_2 17_1 17_2 18_1 18_2 19_1 19_2 20_1 20_2 21_1 21_2 22_1 22_2 23_1 23_2 24_1 24_2 25_1 25_2 26_1 26_2 27_1 27_2 28_1 28_2 29_1 29_2 30_1 30_2 31_1 31_2 32_1 32_2 33_1 33_2 34_1 34_2 35_1 35_2 36_1 36_2 37_1 37_2 38_1 38_2 39_1 39_2 40_1 40_2 41_1 41_2 42_1 42_2 43_1 43_2 44_1 44_2 45_1 45_2 46_1 46_2 47_1 47_2 48_1 48_2 49_1 49_2 50_1 50_2 51_1 51_2 52_1 52_2 53_1 53_2 54_1 54_2 55_1 55_2 56_1 56_2 63_1 63_2 64_1 64_2 65_1 65_2 66_1 66_2 67_1 67_2 68_1 68_2 69_1 69_2 70_1 70_2 71_1 71_2 73_1 73_2 74_1 74_2 75_1 75_2 78_1 78_2 79_1 79_2 82_1 82_2 84_1 84_2 PC
shows a difference between co-inoculated and sequential inoculated wines fermented with the yeast Uvaferm RBS. The wines fermented with Uvaferm VRB correlate as expected with segment 20. Highest loadings on PC2 show the segments 8, 24, 30.

Moreover the segments 1, 2, 14, 22, 23, 31 seem to contribute to the differences of the wines fermented with the yeast Uvaferm RBS. PARAFAC components 3 and 11 from approach 1 explain similar differences between the three starter cultures (Figure 3.16) of the Cabernet Sauvignon data set, where classes for class centroid centering and scaling to intra-class variance were defined according to the three yeast starter cultures. Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

Approach 1 vs approach 2

The results of approach 1 and 2 are very similar. The PARAFAC results from approach 1 are however easier to interpret, especially in terms of the importance of segments for a certain grouping of samples. Moreover the groupings of samples observed from approach 1 appear to be clearer than the groupings from approach 2. PCA from approach 2, on the other side, is easier to model compared to the PARAFAC model regarding the evaluation of the correct number of PARAFAC components.

Approach 2 revealed the difference of the wine sequentially fermented with the yeast-/bacteria combination Lalvin Clos/Enoferm Alpha (clos alpha seq), which was not detected using the unsupervised approach 1.

The fact that the difference of the wine sequentially fermented with the yeast-/bacteria combination Lalvin Clos/Enoferm Alpha (clos alpha seq) was not detected using approach 1 lead to the consideration of implementing class centroid centering and scaling to intra-class variance of each of the compilation matrices Y k (Equation 3.9) into the algorithm of approach 1. The implementation of such a scaling step can bring out the differences between classes better, but it also makes approach 1 to a supervised method. The results of approach 1 with class centroid centered and to intra-class variance scaled compilation matrices Y k (12 classes, one for each treatment) is shown in Figures B.1,B.2,B.3 and B.4 in appendix B.

Deconvolution and identification of compounds in important segments

In targeted analysis known and identified compounds are analysed. In nontargeted analysis, it is sometimes important to know the identity of compounds beforehand, but usually it is not known in advance. Only compounds which contribute to the differentiation of samples are identified (or tentatively identified) after statistical evaluation. For a more in-depth investigation of the data set all important segments evaluated by approach 1 in Section 3.6.2 are more closely examined in the following.

From the discussion in Section 3.6.2 it can be summarized that the components one, two, three, four and eleven from the PARAFAC model with 71 segments are important to explain information on systematic differences between the wines. The segments with congruence loadings higher than 0.5, which can be considered as 'medium to high correlated', are segments 4, 6, 11, 18, 28, 31, 33, 35, 36, 38, 41, 45, 46, 48, 49, 50, 53, 67, 74 and 75 for component one, segments 28, 64, 65, 68, 69, 71 and 78 for component two, segments 1, 4, 8, 11, 14, 22, 23, 24, 30, 31 The number of components of each model was then manually verified using the freely available N-way toolbox [START_REF] Andersson | The N-way Toolbox for MATLAB[END_REF] for MATLAB. The number of factors were checked, and if needed corrected, by examining core consistency, number of iterations until the algorithm converges, residuals, and the interpretability of the loadings. Moreover, non-negativity constraints were applied in the spectra mode.

After exporting all deconvoluted mass spectra using an in-house written MATLAB function, tentative identification of the deconvoluted peaks were performed based on comparison of deconvoluted mass spectra with the NIST 08 spectral library. Furthermore, linear retention indices (LRI) were calculated using a homologous series of n-alkanes and compared with literature values to confirm tentative identifications. Details on the PARAFAC2 models and the identified compounds are summarized in Table 3.4.

PCAs on deconvoluted peak areas

To visualize the above summarized and discussed results three different PCAs were constructed. the second PCA. A one component model was sufficient to explain the differences between the co-inoculated wines and the sequentially inoculated wines. After successively removing all compounds with low loadings on PC1 (small impact on this component) a final one component model was obtained explaining 59.7 % of variance (Figure 3.34(a)). The branched esters isoamyl iso-butyrate [START_REF]BIBLIOGRAPHY[END_REF], isoamyl lactate (19), isoamyl octanoate (87), isoamyl decanoate (121), isoamyl laurate (135) as well as isobutyl octanoate (68) and octanoic acid, 2-methylbutyl ester (89), the straight chain fatty acid ester ethyl octanoate (36), ethyl nonanoate (59), ethyl decanoate (79), ethyl deodecanoate (117), propyl octanoate (61), the two unsaturated ethyl trans-4decenoate (78) and ethyl 9-hexadecenoate (146), the fatty acid decanoic acid (72), the terpenoid nerolidol (111), the unknown long chained fatty acid ester 122 and the unknowns 12,60,88,109,110,115,116 all correlate positively with the co-inoculated wines. and the yeast/lactic acid bacteria combination on the volatile composition of wine have been conducted, but no clear systematic changes have been reported (Antalick et al., 2010a;[START_REF] Gammacurta | Impact of yeast strain on ester levels and fruity aroma persistence during aging of bordeaux red wines[END_REF][START_REF] Abrahamse | Timing of malolactic fermentation inoculation in Shiraz grape must and wine: influence on chemical composition[END_REF][START_REF] Knoll | Impact of different malolactic fermentation inoculation scenarios on Riesling wine aroma[END_REF]. Some authors have observed higher amounts of some esters in co-inoculated wines [START_REF] Abrahamse | Timing of malolactic fermentation inoculation in Shiraz grape must and wine: influence on chemical composition[END_REF][START_REF] Knoll | Impact of different malolactic fermentation inoculation scenarios on Riesling wine aroma[END_REF]. Higher levels of long chain fatty acid esters as well as unsaturated and branched species as a function of malolactic fermentation inoculation mode have, however, not yet been reported. This is most likely due to the fact that long chain fatty acid esters are normally not the focus of targeted methods for general wine aroma analysis. Nevertheless, these compounds were included in the non-targeted approach used here, although this was a priori not specifically known.

Comparison of the new approaches to a reference method

As a reference method, PARAFAC2 was applied to all segments which have not been considered in the above discussed new approach and area values of all integrated deconvoluted peak profiles were analysed using PCA, according to Amigo et al. (2010a). A total of 152 peak area values were obtained in this manner. Figures 3.36 and 3.37 show the scores and loadings plots of PC1 (25.0 % explained variance), PC2

(12.7 % explained variance) and PC3 (11.8 % explained variance) of the autoscaled peak table. Note that only a relatively small proportion of variance is explained, even when compounds with low loadings were successfully removed (not shown). Some structural information is however revealed from the scores plots (Figures 3.36 Lalvin Clos sequentially inoculated with the Enoferm Beta (clos beta seq) correlates most negatively with this PC. The compounds 8, 12, 19, 36, 59, 60, 61, 68, 72, 78, 79, 87, 88 98 109, 101, 111, 115, 116, 117, 121, 122, 135 (co-inoculated, clos PN4 coin). This separation is however not very clear, while there is no valuable information extractable from the loadings plot (Figure 3.36(b)). Similar can be observed for PC3, which also explains differences of the wine made with the yeast/lactic acid bacteria combination Lalvin Clos/Lalvin PN4 (co-inoculated, clos PN4 coin) and of the wines fermented with the yeast/lactic acid bacteria combination Uvaferm VRB/Enoferm alpha (co-inoculated, vrb alpha coin, Figure 3.37).

To obtain more information on the impact of the three yeast starter cultures a PCA on the whole peak table with class centroid centering and scaling to intra-class variance was constructed where classes were defined according to the three yeast starter cultures. The comparability of the results from the new approach using PARAFAC on segmented and mathematically transformed chromatograms in combination with PARA-FAC2 deconvolution of important segments with subsequent PCA, and the deconvolution of all segments using PARAFAC2 and subsequent PCA modelling proves the validity of the results of the new approach. Only 38 segments of the chromatogram turned out to be important for the differentiation of samples using the new approach.

Almost half of the 71 segments had to be deconvoluted using PARAFAC2, which is a considerable time saving. In this study only segments with congruence loadings greater than 0.5 were considered as 'medium to highly correlated' with the raw data.

If, depending on the aim of a study, a higher value is chosen here, such as 0.75, which can be considered as 'highly correlated', even less PARAFAC2 models would have to be constructed and interpreted. The new approach can therefore be considered as a segment selection tool prior to (PARAFAC2) deconvolution of segmented chromatograms. Furthermore, the information on systematic differences obtained from the PARAFAC model on the segmented and transformed chromatograms can be used to study the important segments separately: separate PCAs can be constructed on only compounds from segments which are responsible for a certain grouping of samples. Peak tables obtained in this manner are much smaller than a global peak table from all compounds of the chromatograms and contain less redundant information.

The PCAs constructed on these smaller peak tables are much easier to interpret, as has been shown above.

Conclusions

In this chapter, the two developed data processing approaches have been demonstrated as powerful techniques for the analysis of non-targeted GC-MS data. Both approaches were tested on artificial and real GC-MS chromatograms of multiple samples. The unsupervised approach 1 consists of three steps. First, all chromatograms are segmented and SSCP matrices are calculated for each segment and sample. This transformation of the chromatogram segments into SSCP matrices summarizes information on the variation and covariation of all mass channels in a segment and makes an alignment of peaks unnecessary. The following step, the compilation of the vectorized SSCP matrices into a compilation matrix for all samples in each segment and the transformation of these compilation matrices into SSCP matrices, gives information on the variation and covariation between samples in each segment as a function of the variation and covariation among mass channels in each segment. In the final step these SSCP matrices are merged to a three way array, which is then analysed using PARAFAC.

The supervised approach 2 also consists of three steps.

Step one is, as for approach 1, the segmentation of the chromatogram.

Step two is the singular value decomposition of every segment for every sample and the compilation of the first singular values of each segment into a final matrix. In the third step this matrix is class centroid centered and scaled to intra-class variance using predefined classes of samples and finally analysed using PCA.

A set of 36 chromatograms derived from triplicate HS-SPME-GC-MS analyses of twelve Carbernet Sauvignon wines was used to demonstrate the performance of the data treatment methodologies. Wines for instance could be differentiated according to yeast starter cultures and the inoculation mode of yeast and lactic acid bacteria. Approach 1 is more powerful in revealing clearer discrimination of samples by providing more structural information in the data compared to approach 2.

Compounds responsible for the discrimination of samples could be tentatively identified after deconvoluting peaks in the important segments using PARAFAC2.

Based on the extra information obtained from the PARAFAC components of approach 1, multiple PCAs on the integrated deconvoluted signals of segments which are responsible for a certain grouping of samples provide in-depth insights to the observed phenomena.

The advantage of the novel GC-MS fingerprinting approach 1 presented herein could be confirmed by comparing it with PCA on peak areas from deconvoluted peak profiles of all chromatogram segments. A single PCA on the auto-scaled peak table of all deconvoluted compounds was however not sufficient to summarize all information obtained from the new approach, which underlines the advantage of the new approach 1. The new approache 1 is a fast alternative to conventional data analysis methods, as the only manual tasks are the segmentation of chromatograms and PARAFAC modelling. The new approach can also be seen as a segment pre-selection tool prior to deconvolution of chromatogram segments using e.g. PARAFAC2 or AMDIS. This second fermentation with lactic acid bacteria results in a natural deacidification, enhanced biological stability and improved mouth-feel of wine [START_REF] Ribéreau-Gayon | Handbook of Enology, The microbiology of wine and vinifications[END_REF]. The volatile composition, and as a consequence the sensory properties of wine, are also influenced during MLF. Besides buttery aroma caused by diacetyl, the aroma compound most associated with MLF, other aroma expressions have been reported to be influenced by MLF such as fruity, spicy, toasted and herbaceous notes.

CHAPTER

Clear trends how these notes develop as a function of MLF could however not yet been shown [START_REF] Antalick | Characterization of fruity aroma modifications in red wines during malolactic fermentation[END_REF][START_REF] Gammacurta | Impact of yeast strain on ester levels and fruity aroma persistence during aging of bordeaux red wines[END_REF][START_REF] Costello | Variations in the effect of malolactic fermentation on the chemical and sensory properties of Cabernet Sauvignon wine: Interactive influences of Oenococcus oeni strain and wine matrix composition[END_REF][START_REF] Gámbaro | Effect of malolactic fermentation on the aroma properties of Tannat wine[END_REF][START_REF] Sauvageot | Effects of malolactic fermentation on sensory properties of four Burgundy wines[END_REF][START_REF] Mcdaniel | Sensory panel training and screening for descriptive analysis of the aroma of Pinot Noir wine fermented by several strains of malolactic bacteria[END_REF].

Conventional descriptive profiling techniques such as Quantitative Descriptive Analysis (QDA) are usually performed for the sensory evaluation of experimental wines. These methods, however, require intensive training of panellists and are therefore time-consuming. Moreover, information about the importance of different attributes in the overall perception of panellists is not obtained. An alternative are rapid descriptive methods, which overcome this problem by letting the taster more freely decide how to indicate differences between samples. These fast methods, such as Projective Mapping, have recently gained more popularity. Napping can be seen as a special, restricted and defined case of Projective Mapping [START_REF] Pagès | Recueil direct de distances sensorielles: application à l'évaluation de dix vins blancs du Val-de-Loire[END_REF][START_REF] Dehlholm | Rapid descriptive sensory methods-comparison of free multiple sorting, partial napping, napping, flash profiling and conventional profiling[END_REF]. Napping can be coupled with Ultra Flash Profiling to collect subjects semantic responses such as aroma descriptors, which can be collected as citation frequencies (Pagès, 2005a;[START_REF] Perrin | Comparison of three sensory methods for use with the Napping® procedure: Case of ten wines from Loire valley[END_REF].

In most aroma studies targeted approaches are applied, where a limited set of a priori known and identified compounds is accurately quantified. Considering, that samples can only be compared in terms of these selected compounds, targeted analysis can only confirm or reject an a priori assumption. On the other hand, non-targeted analysis are inherently more comprehensive by taking information of known and unknown compounds into account. By this means, a more holistic picture of the sample composition is obtained. Non-targeted approaches can consequently be more expedient in the search for compounds playing a key role in the differentiation of samples.

Numerous agricultural and food related studies reflect an increasing interest in nontargeted analysis [START_REF] De Vos | Flavour metabolomics: Holistic versus targeted approaches in flavour research[END_REF][START_REF] Cevallos-Cevallos | Metabolomic analysis in food science: a review[END_REF][START_REF] Croley | The chromatographic role in high resolution mass spectrometry for non-targeted analysis[END_REF][START_REF] Wishart | Metabolomics: applications to food science and nutrition research[END_REF][START_REF] Cubero-Leon | Review on metabolomics for food authentication[END_REF].

The in Chapter III developed approach 1 is a fast and effective data analysis method for non-targeted GC-MS fingerprinting of wine volatiles. The goal of this chapter was the merging of fast GC-MS fingerprinting of wine volatiles with the rapid sensory screening method, partial projective mapping including a free choice 123 profiling of wines, to obtain an integrated picture of the sensory and chemical profile of experimental wines. Different MLF scenarios are compared to influence two different Pinotage styles: a fresh, fruity one made from early harvested grapes and a matured, full bodied one made from late harvested grapes. Commercial MLF starter cultures and the inoculation mode (co-inoculation or sequential inoculation) were chosen according common practices in commercial wineries. Two strategies for the merging of GC-MS and sensory data are evaluated: Quantitative and qualitative data matrices obtained from sensory evaluation and chemical fingerprinting are simultaneously analysed using multiple factor analysis (MFA) and the rotation of MFA scores from partial projective mapping onto the PARAFAC sample loadings using general procrusts analysis were evaluated.

Materials and methods

Wine making

To obtain two different Pinotage styles of the 2013 vintage grapes from the same vineyard were harvested at two different dates with differing sugar levels in the Stellenbosch region, South Africa. To obtain a modern, fruity Pinotage style and a full bodied Pinotage style grapes were harvested at 23.5 °B and 26.8 °B, respectively. After destemming and crushing, mashes of the early and late harvested grapes were aliquoted into three treatments with three replicates resulting in a 20 kg fermentation scale. Alcoholic fermentation was conducted using the yeast starter culture ICV-D80 (Lallemand Inc., Canada). 20 g hL -1 yeast starter culture were rehydrated with addition of 30 g hL -1 GoFerm Protect (Lallemand Inc., Canada) and inoculated according to the manufacturers instructions. Sequential inoculation, lactic acid bacteria inoculation after completion of alcoholic fermentation, and co-inoculation, lactic acid bacteria inoculation 24 h after yeast inoculation, were conducted using different com-mercial MLF starter cultures. The following wines were made: co-inoculation with Lalvin VP41, co-inoculation with Lalvin V22 and sequential inoculation with Lalvin VP41 for the early harvested grapes and co-inoculation with Lalvin PN4, sequential inoculation with Lalvin PN4 and sequential inoculation with Lalvin VP41 for the late harvested grapes, respectively (see Table 4.1).

HS-SPME-GC-MS Analysis

All GC-MS analyses were done 15 month after wine making. Headspace solid phase microextraction (HS-SPME) was carried out using a 100 µm polydimethylsiloxane (PDMS) fibre as follows: 5 mL wine sample (pH adjusted to 4.1 using sodium hydroxide solution) was transferred to a 20 mL headspace crimp-top vial and spiked with 152 µg L -1 ethyl hexanoate-d11 as internal standard. Two gram of sodium chloride (preheated to 250 • C) were added and the vial was capped immediately using a PTFE-lined septum and aluminium cap. HS-SPME sampling was done with agitation at 500 rpm for 30 min. Fiber blank and column blank analyses were carried out regularly after 8 injections to confirm that no sample carry-over occurred. To monitor the performance and stability of the system a standard 12 % hydro-alcoholic solution containing some esters and alcohols commonly present in wine (ethyl butanoate until ethyl decanoate, butanol until decanol, isoamyl alcohol, isoamyl acetate, citronellol and nerolidol) was regularly analysed. ing built-in functions. MATLAB was used for all further data analysis. Moreover, the freely available N-way toolbox for MATLAB [START_REF] Andersson | The N-way Toolbox for MATLAB[END_REF] and in-house written MATLAB functions were used. Preprocessing of multi-way arrays was done using the nprocess.m function of the N-way toolbox [START_REF] Andersson | The N-way Toolbox for MATLAB[END_REF]. Parts containing only baseline at the beginning and end of the chromatograms were removed. All GC-MS raw chromatograms were rearranged as matrices of size 3783 × 266 (elution profile × spectral profile). Deconvoluted mass spectra were ex-126 ported as ASCII text files in NIST .msp format using an in-house written MATLAB function and imported into NIST 08 spectral library [START_REF] Stein | NIST Mass Spectral Search Program[END_REF].

GC-MS analyses

4.2.2 GC-MS fingerprinting: Segmentation, mathematical transformation and PARAFAC modelling of GC-MS chromatograms

The data analysis approach 1 for GC-MS fingerprinting described in Chapter 3.4

has been used here. A brief summary will be given in the following. By visually examining TIC overlays of all samples and overlays of all single ion chromatograms for some samples, GC-MS chromatograms are segmented along the retention axis into small sections containing a small number of peaks (approximately one to five peaks).

Sums of squares and cross product (SSCP) matrices are calculated for every segment of each sample. Of each segment the upper triangular part of the obtained SSCP matrices are vectorized and concatenated into a compilation matrix. Subsequently, each of the compilation matrices are transformed into SSCP matrices, which are finally assembled into a three-way array. The final three-way array is of dimensions number of samples × number of samples × number of segments and can be decomposed using PARAFAC. The loadings of mode one and two (sample modes) are identical, as the SSCP matrices of the compilation matrices are symmetric. Systematic differences between samples can be determined by visual examination of the loadings of the sample mode (mode one and two). Congruence loadings [START_REF] Lorho | Generalized correlation loadings: extending correlation loadings to congruence and to multi-way models[END_REF] of the segment mode can be used to identify the importance of segments responsible for the differences between samples. Subsequently, only segments, which contain information on interesting differences between samples, are further investigated. Congruence loadings with an value greater than 0.5 were considered as 'medium to high correlated'. Therefore, only segments with congruence loadings greater than 0.5 on selected PARAFAC components, which show systematic differences between samples, were investigated more in detail.

Deconvolution of important chromatogram segments and identification of compounds using AMDIS

A modification in this chapter to the described methodology in Chapter 3.4 is the deconvolution and identification of peaks in important chromatogram segments (congruence loadings greater than 0.5) with AMDIS [START_REF] Stein | An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data[END_REF]. AMDIS has been used in numerous studies [START_REF] Mallard | AMDIS in the chemical weapons convention[END_REF][START_REF] Fiehn | Metabolic networks of Cucurbita maxima phloem[END_REF][START_REF] Koek | Microbial metabolomics with gas chromatography/mass spectrometry[END_REF][START_REF] Halket | Deconvolution gas chromatography/mass spectrometry of urinary organic acids-potential for pattern recognition and automated identification of metabolic disorders[END_REF][START_REF] Meyer | Automated mass spectral deconvolution and identification system for GC-MS screening for drugs, poisons, and metabolites in urine[END_REF][START_REF] Börner | A high-throughput method for microbial metabolome analysis using gas chromatography/mass spectrometry[END_REF] for detection and deconvolution of GC-MS peaks prior to multivariate modelling. Although the PARAFAC2 approach of Section 3.6.5 of the previous chapter has been reported to be advantages in terms of greater resolution and sensitivity (Amigo et al., 2010a;[START_REF] Murphy | Characterizing odorous emissions using new software for identifying peaks in chemometric models of gas chromatography-mass spectrometry datasets[END_REF], AMDIS was chosen here for the deconvolution as it is easier and faster to apply than the more time consuming PARAFAC2 approach. Moreover, the batch processing function of AMDIS enables automated processing of multiple chromatograms.

Deconvoluted mass spectra were compared with NIST08 library [START_REF] Stein | NIST Mass Spectral Search Program[END_REF]. Linear retention indices (LRI) were calculated using a homologous series of n-alkanes and compared with literature values to confirm tentative identifications.

The batch processing function of AMDIS was used to integrate and export deconvoluted peak areas into text files (.txt). Peak tables obtained from AMDIS were further processed in MATLAB and R. For multivariate analysis the FactoMineR package [START_REF] Lê | FactoMineR: an R package for multivariate analysis[END_REF] of the open source software R (version 3.1.1) was used. Multiple Factorial Analysis (MFA) was carried out with the x-and y-coordinates of the wines of each tasting sheet as a separate table (group) as has been described before [START_REF] Pagès | Collection and analysis of perceived product inter-distances using multiple factor analysis: Application to the study of 10 white wines from the Loire Valley[END_REF]. Aroma descriptors were counted for each wine and grouped together, in a way that for instance all red and black berry attributes were combined as 'red/black fruits'. All aroma descriptors which were named less than five times were excluded from further analysis. In this manner the following five groups were obtained: 'fruitiness', 'vegetal/herbaceous', 'red/black fruits', 'reductive' and 'lactic/butter'. The descriptor groups were included into MFA as a categorical supplementary table as has been descriped by [START_REF] Perrin | Comparison of three sensory methods for use with the Napping® procedure: Case of ten wines from Loire valley[END_REF].

Results and discussion

In this chapter results from the previously developed non-targeted GC-MS fingerprinting methodology (approach 1, Chapter III) of wine volatiles and results from fast projective mapping (including Ultra Flash Profiling) are integrated. The linkage of the information obtained from these two rapid methods facilitates a fast determination of correlations of volatile compounds with aroma descriptor groups. Different strategies of merging the results from Napping and the non-targeted GC-MS analysis will be discussed after the results of the chemical and sensory analysis are discussed separately.

Fermentation performances

A fresh, fruity Pinotage style and a full-bodied Pinotage style were made from grapes of the same vineyard, which were picked at different harvesting dates with differing sugar contents. The wines from early harvested grapes completed alcoholic fermentation within 4 days. Malolactic fermentation in the co-inoculation fermentations with Lalvin VP41 finished 11 days after inoculation, while Lalvin V22 finished 21 days after inoculation. The Lalvin VP41 sequential inoculation finished MLF within 9 days. The starter culture V22 was inoculated at a dosage of 1 g hL -1 , instead of 2 g hL -1 as in the manufactures instruction. As the viable cell numbers on day 3

were around 1 × 10 5 cfu mL -1 , a second inoculate of this treatment on day 7 with the 2 g hL -1 was performed. The initial cell numbers of the fermentations inoculated with VP41 were larger than 1 × 10 6 cfu mL -1 . The wines from late harvested grapes completed alcoholic fermentation within 6 days. The sequentially and co-inoculated wines with Lalvin PN4 completed MLF in 14 days after inoculation. The sequential inoculation with Lalvin VP41 finished MLF after 24 days. The initial cell numbers of the inoculated cultures in all the treatments were greater than 1 × 10 6 cfu mL -1 did not lose any viability until the completion of MLF.

Non-targeted HS-SPME-GC-MS analysis

HS-SPME-GC-MS injections were performed in triplicate. Monitoring of the system stability was carried out throughout the full analysis time to assure the reproducible analyses of all samples, which is particularly important in non-targeted analysis. For this purpose blank injection and a hydro-alcoholic standard solution containing common wine volatiles were injected in regular intervals of eight samples.

Chromatograms were normalized by the total peak area of the internal standard and segmented by examining overlays of all total ion chromatograms (TIC) and overlays of all mass traces of some single injections. Special care was taken that not too many peaks were included into one segment and no peak was allowed to shift into a neighbouring segment. A total of 64 segments were defined in this manner. To examine the impact of the number of segments (segment size) neighbouring segments were combined in a second data set resulting in 32 segments. Mathematical transformation of the segmented chromatograms resulted in two three-way arrays with the dimensions 54 × 54 × 64 and 54 × 54 × 32. The first and the second mode of this array represent the wine samples including three technical replicates for each of the three biological replicates and mode three represents the chromatogram segments.

To ascertain the correct number of components, multiple PARAFAC models with two to 20 components were calculated with ten repetitions to evaluate stability and convergence time of each model. Furthermore, core consistency diagnostic [START_REF] Bro | A new efficient method for determining the number of components in PARAFAC models[END_REF], residuals, captured variance and convergence time of the algorithm were used to identify the correct number of components. The PARAFAC models of the 54 × 54 × 64 array revealed more systematic differences compared to the models of the 54 × 54 × 32 array. Any further discussion and representation of results is Examination of the loadings of the sample modes (first and second modes) of the PARAFAC model revealed that three components contain information on systematic differences, while other components reflect only non-systematic information. See Section 3.6.2 for a previous discussion on PARAFAC components which reflect only non-systematic differences among samples. All components explaining non-systematic structure in the data are not further discussed.

Component one explaining 11.5 % of variation reflects the difference between the wines made from early and late harvested grapes (Figure 4.1). The triplicate injections of one of the biological replicates of the wine from early harvested grapes co-inoculated with the MLF starter culture V22 differ from the other two biological replicates in terms of higher loadings on component one. These Differences are mainly explained by the segments 1, 2,5,6,7,11,13,14,15,17,22,23,24,25,26,29,34,38 and 49 (congruence loadings larger than 0.5). Component five (5.8 % explained variation) separates the wine made from early harvested grapes co-inoculated with the MLF starter culture Lalvin VP41 from all other wines (Figure 4.1). The segments 1, 2, 3, 5, 13, 14, 23, 26, 29, 34, 37, 41 and 61 show high congruence loadings on component five. Component six (4.3 % explained variation) separates the wine made from late harvested grapes co-inoculated with MLF starter culture Lalvin PN4 from all other wines (Figure 4.2). The segments 4, 5, 35, 37 and 62 have congruence loadings larger than 0.5 on this component. To obtain more detailed information on compounds which are responsible for the differentiation of samples, all segments with congruence loadings larger then 0.5 were deconvoluted using AMDIS [START_REF] Stein | An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data[END_REF][START_REF] Behrends | A software complement to AMDIS for processing GC-MS metabolomic data[END_REF]. Significantly different (ANOVA, α = 0.05, technical replicates were averaged) peak areas of deconvoluted peaks among the six wines were compiled in a peak table (see Table 4.2).

Sensory analysis

Partial projective mapping provides a holistic view on groupings and sensory characteristics of the tasted wines. As a member of the multi-block PCA family, MFA focuses on the analysis of several sets of variables (blocks or groups) which are collected on the same set of observations (samples). MFA is therefore the method of choice for the analysis of projective mapping data, especially when qualitative variables such as frequencies of sensory descriptor groups have to be incorporate into the analysis [START_REF] Perrin | Comparison of three sensory methods for use with the Napping® procedure: Case of ten wines from Loire valley[END_REF]Pagès, 2005a). The representation of wines (scores of the global PCA) is shown in Figure 4.3(a). PC1 explaining 41.0 % of variance separates the wines according to early and late harvested wines. The second PC (23.1 % explained variance) shows differences between the wines from late harvested grapes co-inoculated with the MLF starter culture Lalvin PN4. The correlation of sensory descriptors is displayed in Figure 4.3(b). Overall fruitiness highly correlates with the 133

Merging of chemical and sensory data

To shed light onto the linkage of volatile compounds, assessors' ratings and sensory descriptors information of the fast sensory screening and the fast GC-MS screening of volatiles have to be merged. One possibility is the incorporation of the peak table obtained from GC-MS fingerprinting into the MFA of the partial projective mapping.

The peak area values are, however, qualitatively different from the assessors' ratings and are therefore not supposed to be added as active elements into MFA, but can be projected as supplementary tables. Supplementary variables have no influence on the MFA, but they can be helpful for the interpretation of results and/or the linkage of other data. Descriptor frequencies are included as a supplementary table into MFA in the same way. The integration of the auto-scaled peak table into MFA of the partial projective mapping data is shown in Figure 4.3(c). A vast majority of compounds correlate negatively on PC1 with the wines obtained from early harvested grapes (Figure 4.3(a)) and the overall fruitiness (Figure 4.3(b)). These compounds are mainly branched fatty acid esters and acetates, which are known to contribute to fruity notes in wines [START_REF] Ribéreau-Gayon | Handbook of Enology. The chemistry of wine and stabilisation and treatments[END_REF]. Unknown compound no. 25, unknown compound no. 4 with an terpenoid-like mass spectra and unknown compound no. 31 with the long chain fatty acid alike mass spectra are positively correlated, while the unknown compound no. 16 is negatively correlated with the wine made from late harvested grapes co-inoculated with the MLF-starter culture Lalvin PN4 and the aroma descriptor vegetal/herbaceous.

The direct linkage of the PARAFAC loadings with the results from MFA on the perceptual maps of all tasters and descriptor frequencies for each wine can also be of great importance, especially when certain sensory attributes are in the focus of a study. The main focus of this study for instance was the investigation of the impact of MLF on two Pinotage styles. MFA results clearly showed that the wine obtained from late harvested grapes co-inoculated with Lalvin PN4 solely correlates with veg- etal/herbaceous notes. GC-MS fingerprinting also revealed differences between this wine and the others. Merging of these results can be obtained by rotating the common factor scores of the MFA onto components of the PARAFAC loadings of the sample mode using general procrustes analysis (GPA). Note that the number of MFA components and PARAFAC components have to be the same. The representation of the sensory descriptors can subsequently be counter rotated according to the rotation of the common factor scores. This procedure was applied to the first and second principle component of the MFA results, which were rotated onto PARAFAC component one and six using GPA. A good match of the rotated common factor scores (PC1 and PC2) with the PARAFAC loadings of the segment mode (component 1 and 6) is shown in figure 4.4(a). The counter rotated representation of sensory descriptors from MFA is shown in Figure 4.4(b)). The obtained results are comparable with the above discussed MFA where the peak table of deconvoluted peaks was included into the MFA as supplementary table. The direct merging of PARAFAC results from GC-MS fingerprinting and MFA from partial projective mapping using GPA can be beneficial in terms of time when only selected informations are of a greater interest, for instance if in the here presented example the sole focus would have been on wines that correlate with vegetal/herbaceous notes, only the segments correlated with the wine made from late harvested grapes co-inoculated with Lalvin PN4 were needed to be deconvoluted and further investigated.

Conclusions

The new non-targeted data analysis approach (approach 1) was applied to study the impact of different MLF scenarios to a fresh, fruity and a full-bodied Pinotage style. Sensory evaluation of the wines was carried out using the rapid descriptive methods partial projective mapping with free choice profiling (Ultra Flash Profiling).

By merging the results of the non-targeted GC-MS analysis of volatiles and the sensory data a more holistic overview of the aroma properties of the wines was obtained.

Moreover, correlations of aroma descriptor groups and volatile compounds could be demonstrated. The attribute 'fruitiness' for instance showed a high correlation with the wines made from early harvested grapes, as well as with many ester, which are well known for their contribution to fruity notes in wine. Moreover, the wine made from late harvested grapes co-inoculated with the Lactic Acid Bacteria (LAB) starter culture Lalvin PN4 was rated as very different from the assessors than the other wines made from late harvested grapes. The descriptor 'vegetal/herbaceous' correlated highly with this wine, as well as with an unknown compound with an tepenoid-like mass spectra. From essential oils it is well known that terpenoid compounds can have 'herbaceous' aroma characteristics. The correlated compounds could therefore be responsible for the 'vegetal/herbaceous' note in the wine made from late harvested grapes co-inoculated with the LAB starter culture Lalvin PN4, but at least they are markers for the differences of this wine.

CHAPTER V

Application 2: Full factorial aroma study on the impact of grapevine age, yeast strain and must turbidity on the aroma of Riesling experimental wines

Introduction

It is widely assumed that the age of grapevines has a positive effect on the quality of the wine, but not much research has been conducted on this topic. In a recent study, on six red and white cultivars, wines from older grapevines generally had higher levels of titratable acidity (TA) and a better tannic structure compared to wines made from young grapevines [START_REF] Zufferey | Age de la vigne II. Influence sur la qualité des raisins et des vins[END_REF]. Another study on Beihong wines showed an increase in the concentration of total volatiles and odour activity values (OAVs) for wines produced from older grapevines [START_REF] Du | Effect of Grapevine Age on the Aroma Compounds in 'Beihong' Wine[END_REF]. However, these differences might be more or less evident depending on the vintage [START_REF] Reynolds | Interactions of vine age and reflective mulch upon berry, must, and wine composition of five Vitis vinifera cultivars[END_REF]. The impact of the age of grapevines on the sensory properties and composition of volatile compounds is still poorly understood.

Two very important oenological factors that are know to influence wine aroma are the yeast strain used for alcoholic fermentation and the degree of must turbidity prior to fermentation. Must turbidity positively influences the fermentation rate and the final degree of fermentation. On the other hand, depending on the degree of turbidity the sensory characteristics and overall quality of wines can be negatively effected.

Must clarification should therefore be effective, but not too drastic [START_REF] Singleton | Composition and sensory qualities of wines prepared from white grapes by fermentation with and without grape solids[END_REF][START_REF] Groat | Effects of insoluble solids added to clarified musts on fermentation rate, wine composition, and wine quality[END_REF][START_REF] Losada | Influence of some prefermentative treatments on aroma composition and sensory evaluation of white Godello wines[END_REF][START_REF] Williams | White wine composition and quality as influenced by method of must clarification[END_REF][START_REF] Ribéreau-Gayon | Handbook of Enology, The microbiology of wine and vinifications[END_REF]. There is a broad agreement in literature that concentrations of higher alcohols increase as a function of must turbidity. The same applies for concentrations of ethyl and acetate esters [START_REF] Houtman | The effect of juice clarity and several conditions promoting yeast growth on fermentation rate, the production of aroma components and wine quality[END_REF][START_REF] Nicolini | Effect of juice turbidity on fermentative volatile compounds in white wines[END_REF][START_REF] Losada | Influence of some prefermentative treatments on aroma composition and sensory evaluation of white Godello wines[END_REF][START_REF] Karagiannis | Insoluble grape material present in must affects the overall fermentation aroma of dry white wines made from three grape cultivars cultivated in Greece[END_REF]. The degree of turbidity can also have an impact on varietal aroma compounds and glycoconjugates [START_REF] Moio | Influence of clarification treatment on concentrations of selected free varietal aroma compounds and glycoconjugates in Falanghina (Vitis vinifera L.) must and wine[END_REF]. [START_REF] Karagiannis | Insoluble grape material present in must affects the overall fermentation aroma of dry white wines made from three grape cultivars cultivated in Greece[END_REF] showed that the influence of turbidity on the volatile composition of wines also depends on the grape variety.

The impact of the yeast strain on the sensory and volatile profile of wines has been intensively studied and reviewed [START_REF] Rapp | Wine aroma[END_REF][START_REF] Romano | Function of yeast species and strains in wine flavour[END_REF][START_REF] Swiegers | Yeast and bacterial modulation of wine aroma and flavour[END_REF][START_REF] Lambrechts | Yeast and its importance to wine aromaa review[END_REF][START_REF] Antonelli | Yeast influence on volatile composition of wines[END_REF][START_REF] Patel | Effect of different strains of Saccharomyces cerevisiae on production of volatiles in Napa Gamay wine and Petite Sirah wine[END_REF]. The conversion of must ingredients to sensorially important metabolites during fermentation such as acids, alcohols, carbonyl compounds, esters, sulfur compounds and monoterpenoids depends highly on the yeast strain [START_REF] Swiegers | Yeast and bacterial modulation of wine aroma and flavour[END_REF].

The vast majority of studies focusing on the impact of oenological and viticultural practices on wine composition are conducted in a way that all parameters are kept constant and only the factor under study is varied. In this way interaction effects between factors are completely neglected, which can lead to biased conclusions. The usage of multifactorial design in experimental wine making is expedient, as it facilitates the evaluation of multiple factors at the same time. Full factorial designs test all possible conditions and can be used to find both main effects and interaction effects [START_REF] Box | Statistics for experimenters: design, innovation, and discovery[END_REF].

In Chapter IV the multi-block PCA method MFA has been shown to be expedient to link data obtained from HS-SPME-GC-MS fingerprinting and sensory data from partial projective mapping with free choice profiling (Ultra Flash Profiling). Merging of data of these to fast methods provides an integrated view on and aroma of wines. The primary goal of this chapter was the combination of the strategy used in Chapter IV with a full factorial winemaking design for the detailed investigation of the impact of grapevine age, yeast strain and must turbidity and the dependencies among these factors (main and interaction effects) on the volatile composition and aroma of Riesling experimental wines. This combination will be shown to provide a powerful methodology for the detailed investigation of viticultural and enological factors influencing the aroma of wine.

Materials and methods

Viticulture

The experimental vineyard in Geisenheim, Germany, planted with Vitis Vinifera L. cv. Riesling vines of the clone 239-17 grafted on 5C Teleki rootstock. The grapevines were planted in 1971. In 1995 several rows were uprooted and replanted with grapevines of the same clone and rootstock. The result for the vintage 2013 is a vineyard with alternating blocks of vines that are 42 and 18 years old. Grapes of old and young grapevines were used separately for experimental wine making.

Experimental design and wine making

To obtain information on the effects of the three factors grapevine age, must turbidity and yeast strain on the volatile composition of Riesling wines, a 2×2×3 full factorial design was used for the experimental wine making. The structure of the full factorial design is presented in Table 5.1 and the 2×2×3 coded model matrix is presented in Table 5.2. Multiple linear regression (MLR) was used to quantify main effects and one second order interaction effect.

The complete MLR model equation for the 3 factors (X 1 , X 2 and X 3 ) is:

Y = b 0 + b 1 X 1 + b 2 X 2 + b 3A X 3A + b 3B X 3B + b 12 X 1 X 2 (5.1)
where Y is the response variable and b 0 is the constant term. The coefficients b 1 , b 2 and b 3 account for the main effects of the factors X 1 , X 2 and X 3 , respectively.

Moreover, the coefficient b 12 represents the second order interaction term of the factors X 1 and X 2 . Note that second order interaction effects of categorical factors with more than two levels (as the factor yeast X 3 ) cannot be calculated.

All twelve postulated fermentations in the model matrix (Table 5.2) were done in four replicates. Furthermore, duplicate HS-SPME-GC-MS analyses were performed for each of the fermentations, resulting in 96 analyses. All GC-MS injections were performed in random order.

b 0 b 1 b 2 b 3A b 3B b 12 1 1 -1 -1 0 0 1 2 1 -1 -1 0 1 1 3 1 -1 -1 1 0 1 4 1 -1 1 0 0 -1 5 1 -1 1 0 1 -1 6 1 -1 1 1 0 -1 7 1 1 -1 0 0 -1 8 1 1 -1 0 1 -1 9 1 1 -1 1 0 -1 10 1 1 1 0 0 1 11 1 1 1 0 1 1 12 1 1 1 1 0 1 145

HS-SPME-GC-MS analysis

All GC-MS analyses were conducted 17 month after winemaking. For headspace solid phase microextraction (HS-SPME) a 100 µm polydimethylsiloxane (PDMS) fibre was used. A standard SPME procedure typical for wine analysis was followed. Five millilitres of the wine sample were pipetted into a 20 mL headspace crimp-top vial together with two grams of sodium chloride (preheated to 250 • C and cooled to room temperature). The sample was spiked with 152 µg L -1 ethyl hexanoate-d11 as internal standard and the vial was capped immediately using a PTFE-lined septum and an aluminium cap. Each wine sample was extracted at 500 rpm for 10 min. To confirm that no sample carry-over occurred, fibre and column blanks were run regularly after eight injections. Moreover, a standard 12 % hydro-alcoholic solution containing some esters and alcohols commonly present in wine (including ethyl butanoate until ethyl decanoate, butanol until decanol, isoamyl alcohol, isoamyl acetate, citronellol and nerolidol) was regularly analysed to ensure constant and stable performance of the system. Linear retention indices were determined using a series of n-alkanes and compared to literature values to confirm tentative peak identification using the software AMDIS [START_REF] Stein | An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data[END_REF] based on deconvoluted mass spectra. Each GC-MS injection was performed in duplicate and all chromatographic analyses were run in random order. PARAFAC can mathematically been seen as a three-way generalization of bilinear factor or component models such as PCA [START_REF] Harshman | PARAFAC: Parallel factor analysis[END_REF]. The first and second modes of the obtained PARAFAC model represent the samples, similar to PCA scores. In multi-way terminology, however, only the word 'loading' is used.

GC-MS

The modes one and two are identical, since the SSCP matrices which were compiled into a three-way array are symmetrical. The loadings of the third mode, representing the chromatogram segments, are provided as congruence loadings. Calculations were conducted using MATLAB version 8.0 (R2012b, The MathWorks Inc., Natick, MA, similar close to each other and wines that they perceived as different away from each other on a 59.4 × 84.1 cm (A1) sheet of paper. Furthermore, all panellists were asked to write aroma descriptors of their own choice next to each wine.

The x-and y-coordinates of the positions of wines were measured. Sensory descriptors were collected for every wine sample and grouped according to similarity.

From these descriptor groups, only those that had four or more entries for at least one wine sample were kept for further analysis. These groups were: 'clean/typical', 'fruity notes', 'floral notes', 'sweet notes', 'musty', 'ripe/apple/oxidized' and 'reductive notes'.

For Multiple Factor Analysis (MFA) the FactoMineR package [START_REF] Lê | FactoMineR: an R package for multivariate analysis[END_REF] of the open source software R (version 3.1.1 (R Core Team, 2014)) was used. The x-and y-coordinates of each tasting sheet were defined as separate tables (blocks), while the descriptor groups were included into MFA as a categorical supplementary table as has been described by [START_REF] Pagès | Collection and analysis of perceived product inter-distances using multiple factor analysis: Application to the study of 10 white wines from the Loire Valley[END_REF] and [START_REF] Perrin | Comparison of three sensory methods for use with the Napping® procedure: Case of ten wines from Loire valley[END_REF]. The additional chemical data from FT-IR analysis and peak area values from important chromatogram segments (PARAFAC congruence loadings > 0.3 ) were also included into MFA as supplementary tables. More statistical background on MFA can be found elsewhere [START_REF] Salkind | Encyclopedia of measurement and statistics[END_REF][START_REF] Escofier | Multiple factor analysis (AFMULT package)[END_REF].

Results and discussion

The current study extends previous work from Chapter III and Chapter IV on the development of a non-targeted GC-MS screening method with experimental wine making using a full factorial experimental design and fast sensory profiling. This study provides new insights into main and interaction effects of the three factors grapevine age, yeast starter culture and must turbidity on the aroma composition of Riesling wines.

Fermentation performances

Each of the three studied factors had an impact on the fermentation process of the different treatments. Figure 5.1 shows the fermentation kinetics of all twelve treatments. All turbid musts fermented quicker and reached a high final degree of fermentation (less than 1 g L -1 residual sugar), while all fermentations of the clear musts proceeded slower and 'stuck' (fermentation has stopped before all the available sugar was metabolised) at the end of the fermentation. Final residual sugar contents of wines from clear musts of young and old grapevines were 17.9 g L -1 and 9.5 g L -1

for Oenoferm Klosterneuburg, 12.8 g L -1 and 7.3 g L -1 for Zymaflore X5, and 5.5 g L -1 and 4.3 g L -1 for EC 1118. Wines made from clear must of old grapevines had systematically lower residual sugar contents. All musts from old grapevines, irrespective of their turbidity, also showed slightly higher fermentation rates compared to the musts from younger grapevines, with the exception of the wine made from clear musts fermented with the yeast EC 1118. This treatment did not show any differences in fermentation kinetics between the musts from old and young grapevines. The starter culture EC 1118 also generally showed a better fermentation performance compared to the starter cultures

Data analysis GC-MS fingerprinting

The segmentation of chromatograms was done by examining overlays of all total ion chromatograms (TIC), while special care was taken that not too many peaks were included into each segment and no peak was allowed to shift into a neighbouring segment. In this manner 56 regions in the chromatograms were defined. Subsequently, every two neighbouring segments were combined to evaluate the impact of segment size resulting in one data set with 56 smaller segments and one with 28 larger segments. Mathematical transformation of the segmented chromatograms resulted in two three-way arrays with the dimensions 96 × 96 × 56 and 96 × 96 × 28. Here the first and second mode represent the wine samples (including replicates) and the third mode represents the chromatogram segments. Multiple models were built to find a PARAFAC model with an appropriate number of components. Each model was repeated 10 times to evaluate the stability and convergence time of each model. Moreover, the core consistency diagnostic [START_REF] Bro | A new efficient method for determining the number of components in PARAFAC models[END_REF], residuals, and captured variance were examined. Outliers in the sample as well as in the segment mode were identified using Hotelling's T-Square statistics, by examining residuals and loadings, and were subsequently removed. The final three-way arrays after exclusion of outliers had the dimensions 95 × 95 × 49 and 95 × 95 × 22. The first two modes were mean centered and the last mode was scaled to unit variance using the nprocess.m function of the N-way toolbox. The PARAFAC models on both arrays revealed the same information on systematic differences among samples. The size of the segments had no influence on the quality of the PARAFAC results. Only the results of the PARAFAC model on the 95 × 95 × 22 array is therefore discussed and represented in the following. A 14 component PARAFAC model gave the best interpretable results by explaining 89.8 % of the total variation in the dataset.

MLR and visual interpretation of PARAFAC components

In addition to visual examination of the PARAFAC loadings of the sample mode, multiple linear regression (MLR) was used as a complementary means to evaluate the effects of the three studied factors, as the results of the PARAFAC model can be interpreted as a projection of the raw data to a much lower dimensional space (projection to a few 'latent variables'). The PARAFAC loadings of the sample mode represent condensed information of the variation between samples. For each PARAFAC component a MLR model was therefore calculated using the PARAFAC loadings of the sample mode (duplicate injections were averaged) as the response variables (dependent variables) and the design matrix (Table 5.2) as independent variables. The MLR coefficients reflect a quantitative measure of a factor on the response variable. The higher a coefficient the higher its impact on the response variable. The coefficients for the two dummy variables representing the first and the second yeast starter cultures must be interpreted in relation to the third yeast starter culture, which is represented as zero (See coding in Table 5.2). For instance, if b 3A is positive and b 3B is negative, then yeast one gives the highest response, followed by yeast three and yeast two gives the lowest response. 5.1. Factor X 1 : age of vines, factor X 2 : must turbidity, factor X 3 : yeast strain (see 5.3). All segments with congruence loadings larger than 0.3 were considered to be 'week to strongly correlated' with the raw data, and therefore considered as important.

The coefficients of the MLR on the sample loadings of PARAFAC component one show that the factors must turbidity (X 2 ) and yeast strain (X 3 ) are significant (Figure 5.2(a)). The absolute size of the coefficients show that turbidity has the highest impact, while the effect of the yeast is slightly smaller. All coefficients, except of the interaction between the age of grapevine and must turbidity (X 1 *X 2 ), of the MLR on the sample loadings of PARAFAC component two are highly significant (Figure 5.2(b)). The impact of the yeast starter cultures (X 3 ) is however four to five times higher than the impact of the age of grapevines (X 1 ) and must turbidity (X 2 ). The effects of the studied factors on the sample loadings of PARAFAC component seven is shown in Figure 5.2(c). The coefficients of X 1 , X 2 and X 3B (age of grapevines, must turbidity and yeast Zymaflore X5) are highly significant and have equal total values. Moreover, the interaction between the factors age of grapevines and must turbidity (X 1 *X 2 ) is significant, albeit very small. The fourth MLR model on the sample loadings of component ten shows that the yeast starter culture Zymaflore X5 has a large impact in this case (Figure 5.2(d)), while all other factors have minimal effect, albeit the factors age of grapevines (X 1 ) and must turbidity (X 2 ) are significant.

The examination of the MLR models showed clear main effects of the studied factors.

The second order interaction effect between the factors age of grapevines and must turbidity (X 1 *X 2 ) was negligibly small on all PARAFAC components. Coefficients for interaction effects between the yeast strain (a categorical factor with three factor levels) and the other two factors age of grapevines and turbidity cannot be calculated.

A quantitative measure for these interactions can therefore not be provided. These dependencies of the factors can however be assessed by visual examination of the PARAFAC loadings.

What can be learned from the visual examination of the four important PARAFAC components will be discussed in greater detail in the following. Component one explaining 13.2 % of the variation in the dataset mainly reflects differences between the wines made from turbid and clear musts (Figure 5.3) 3, 4, 6, 7, 11, 12, 15, 16, 19, 20, 23 and 24, which all have congruence loadings higher than 0.3 on the segment mode of the PARAFAC components one, two, seven and ten, were examined more closely to investigate the compounds responsible for the differences between groups of samples.

A very conservative value of 0.3 was chosen here, which can be interpreted as a 'weak to strong correlation' with the raw data. The software package AMDIS was used to deconvolute coeluting peaks in each of the important segments. All peaks which showed significant (ANOVA, α = 5 %) differences among treatments were tentatively identified by comparing deconvoluted mass spectra with the NIST 08 spectral library. Moreover, linear retention indices (LRI) were calculated using a homologous series of n-alkanes and compared with literature values to confirm tentative identifications.

All compounds are summarized in Table 5.3.

PCA on deconvoluted peak areas

To verify the information obtained from the PARAFAC approach and to gain more detailed information on the compounds responsible for the discrimination between samples, a final PCA (with autoscaling) on the peak areas of all compounds was calculated. The first four principal components (PCs) reflect almost the same information as obtained from the PARAFAC approach regarding discrimination between groups of samples. Scores and loadings plots of all PCs (all plotted against PC1) are shown in Figures 5.6,5.7 and 5.8. In line with the first component of the PARAFAC model, the first PC, explaining 52.0 % of the total variance, separates treatments according to the degree of turbidity of the musts (Figure 5.6). PC1 correlates positively with the wines obtained from turbid musts as well as the branched alcohols isobutanol (compound 2), 2-methylbutanol (compound 3) hexanoate (compound 25), isoamyl octanoate (compound 29), isoamyl pentanoate (compound 32) and the unknown compound 21, whereas the equivalent wines made from young grapevines grouped together with the wines from turbid must fermented with the other two starter cultures.

The differences between the wines from musts from old and young grapevines which could be observed in PARAFAC component 7 are associated with PC3 and PC4 (7.4 % and 5.6 % explained variance,respectively;Figure 5.7 and 5.8). The wines made from clear musts obtained from old grapevines fermented with the yeasts EC1118 and Oenoferm Klosterneuburg correlate positively with hexyl acetate (compound 14), ethyl 9-decenoate (compound 27) and the unknown compound 31. These wines also correlate negatively with the unknown compound 21.

Overall the results of the PCA based on chromatographic peak areas are comparable to those of the PARAFAC approach. However, information on the differentiation of the wines made with the starter culture Zymaflore X5 which could be observed on PARAFAC component 10 could not be extracted from PCA data, although all wines made with this yeast have significantly higher (2-3 fold) levels of the compound ethyl 2-hexenoate (compound 17), as shown in the boxplot in Figure 5.9. A similar scenario has been described in Chatper III, where the PARAFAC approach used here for pro-163 cessing of GC-MS chromatograms was superior to PCA performed on deconvoluted peak area values. Ethyl 2-hexenoate (compound 17) is assumed to be a varietal ester formed from precursors located in the grape skins [START_REF] Antalick | Bilan biochimique et sensoriel des modifications de la note fruitée des vins rouges lors de la fermentation malolactique: rôle particulier des esters[END_REF]. Levels of this ester can be effected among others by yeast [START_REF] Liang | Aromatic and sensorial profiles of young Cabernet Sauvignon wines fermented by different Chinese autochthonous Saccharomyces cerevisiae strains[END_REF] and leaf removal in the vineyard ( Šuklje et al., 2014).

Merging of chemical and sensory data

MFA on the x-and y-coordinates of wines from partial projective mapping revealed systematic differences among samples similar to those obtained from the GC-MS fingerprinting of volatiles. In order to establish links between chemical and sensory data, the citation frequencies of descriptor groups (how often a descriptor was mentioned for a wine), the area values of deconvoluted peaks and additional chemical data from FT-IR analysis were integrated into MFA as supplementary tables. After outlier removal, 14 of the 18 tasters were included in the MFA. The representation of wines is given in Negative sensory effects of highly turbid musts have been often described in literature [START_REF] Singleton | Composition and sensory qualities of wines prepared from white grapes by fermentation with and without grape solids[END_REF][START_REF] Groat | Effects of insoluble solids added to clarified musts on fermentation rate, wine composition, and wine quality[END_REF][START_REF] Houtman | The effect of juice clarity and several conditions promoting yeast growth on fermentation rate, the production of aroma components and wine quality[END_REF][START_REF] Losada | Influence of some prefermentative treatments on aroma composition and sensory evaluation of white Godello wines[END_REF]. All wines made from clear musts but also the wine X5 Y T (turbid must from young grapevines fermented with Zymaflore X5) correlate negatively with above-mentioned compounds and positively with the sensory descriptor groups 'clear/typical' and 'fruity notes'.

The samples EC1118 O T and X5 O T (turbid musts from old vines, fermented with Zymaflore X5 and EC1118) are the only treatments where the grapevine age 5.3) and data from FT-IR (d) were included as continuous supplementary variables.

ethyl acetate (compound 1) and hexyl acetate (compound 14), the unsaturated ethyl ester ethyl 9-decenoate and two unknown compounds 24 and 31 correlate with these wines (Figure 5.10(c)). The same compounds also correlated with these samples on PC1 and PC2 of the PCA performed on the autoscaled peak table (Figure 5.6).

The results presented here reveal that the decrease in overall wine quality observed in sensory analysis is not only dependant on the turbidity of musts, but also depends on the yeast strain and the composition of the must (musts from old grapevines vs. musts from young vines). Effects of the studied factors (grapevine age, must turbidity and yeast strain) and how they influence each other highlight the benefits of the multifactorial approach used in this study. The results show that similar information on the grouping of wines were obtained from GC-MS fingerprinting and partial projective mapping. By incorporating area values of the peaks that contributed to differences among samples in the PARAFAC model into MFA, correlations of the compounds with sensory descriptor groups could be found. However, correlations between volatile compounds and aroma descriptor groups have to be interpreted with some caution, as correlations do not necessarily imply causality.

Conclusions

To obtain a better understanding of the importance of viticutural and oenological factors and their interactions on the composition of wine aroma compounds, experimental wine making in combination with sensory and chromatographic analysis is essential. In this study, non-targeted HS-SPME-GC-MS fingerprinting and partial projective mapping with free choice descriptor profiling were combined with full-factorial design of experimental wine making to allow an in-depth study of the impact of the age of vines, must turbidity and yeast starter culture on the volatile composition and the aroma of Riesling wines. The applied GC-MS fingerprinting approach (approach 1, Section 3.4), including segmentation and transformation of chro-matogram segments resulting in deconvoluted peak profiles for each segment and sample. Building and evaluating one multivariate model for each chromatogram segment for all (or even each) sample is however very time consuming. The two approaches (algorithms) described in Chapter three take peak shifts among samples into account and are applied to the entire chromatograms (all predefined chromatogram segments) of all samples. The results reveal information on systematic differences among samples and the importance of chromatogram segments contribution to differences among samples. Only these important chromatogram segments containing information on differences among samples can then subsequently be deconvoluted, if further information on the chemical compounds in these segment is needed. This represents a vast saving in time as only a small number of important segments has to be deconvoluted.

Both approaches use segmentation of the chromatograms and subsequent transformation of the two-dimensional chromatogram segments of each sample (mass spectral profile × elution profile) into sums of squares and cross product matrices (SSCP; mass spectral profile × mass spectral profile). The sums of squares are a measure of variation within a mass channel, whereas the cross products are a measure of covariation between two mass channels. Note, that SSCP matrices are similar to variancecovariance matrices. The SCCP matrices of chromatogram segments with peaks of the same concentration in different samples remain constant, even when the location (retention time) of the peaks are different among samples. Besides the described segmentation and transformation, approach one includes further mathematical rearrangements resulting in a three-way array which can be decomposed using parallel factor analysis (PARAFAC). Visual examination of the PARAFAC loadings reveals sample groupings and important segments responsible for the groupings can be identified. Approach two is also based on segmentation of the chromatograms. Each segment is automatically decomposed using singular value decomposition (SVD), which is an eigenvalue decomposition of the SSCP matrix of the chromatogram segment.

Only the first singular value (or values) are used for further PCA analysis. Similar to approach one, PCA scores show groupings of the samples, while the loadings provide information on segments responsible for the grouping. Based on the results of approach one and approach two, important segments responsible for the grouping between samples, can be further deconvoluted, if more detailed information on the compounds in these segments is needed. The here developed approaches can also be considered as a segment selection tools for the deconvolution of chromatogram segments, as the number of segments for deconvolution is largely reduced compared to the deconvolution of all segments of a chromatogram.

The two approaches have been tested on an artificial data set and on a real HS-SPME-GC-MS data set of wines fermented with different yeast and malolactic fermentation scenarios. The results were compared to each other and validated with a reference method (PARAFAC2 deconvolution of all chromatogram segments with subsequent PCA). Both approaches are suitable for finding systematic differences among samples. The PARAFAC model in approach one is more difficult to model, whereas in approach two only SVD (and PCA) is utilized. Approach one, however, reveals more structure in the data then approach two. Moreover, even the PCA results from deconvoluted peaks of all segments (reference method) showed less information on differences among samples then the results of approach 1. Approach 1 is therefore a fast and more effective alternative to conventional data analysis methods. The suitability of approach 1 for large data sets, such as metabolomics data, where samples are analysed in multiple sequences and contain therefore more shifting peaks has to be still investigated. The only manual tasks of approach 1 are the segmentation of chromatograms and PARAFAC modelling. Automated segmentation would be however essential for the analysis of LC-MS data, where a visual segmentation of the chromatograms would not be possible due to the much bigger mass-to-charge range (50 -4000 u) compared to GC-MS data (30 -500 u). Approach 1 has been applied in the further course of the thesis to study relevant topics in wine research.

In Chapter four, approach one was used to study the effect of different malolactic fermentation scenarios on the volatile composition of a fresh, fruity Pinotage style and a full bodied Pinotage style. Moreover, sensory evaluation of the wines was carried out using the rapid descriptive method projective mapping (similiar to napping) with free choice descriptor profiling (Ultra Flash Profiling). Merging of the results of GC-MS fingerprinting and perceptual mapping by means of multiple factor analysis (MFA) provided a comprehensive integrated overview of the volatile composition and the sensory expression of the wines. Correlations of volatile compounds and sensory attributes were found. A high correlation was found between the attribute 'fruitiness' and many esters, which are well known for their contribution to fruity notes in wine.

Furthermore, one wine mainly described with the descriptor 'vegetal/herbaceous' and correlated with higher concentrations of an unknown compound with an terpenoidlike mass spectra. The presented method was proven to be a fast and powerful tool to obtain a broad overview on the sensory characteristics and the volatile composition of experimental wines.

In Chapter five, the second application also involved the combined data evaluation of the perceptual mapping data and the GC-MS fingerprinting data obtained from approach one for a set of Riesling experimental wines. In this chapter, the experimental wine making was done in a full factorial design to allow in-depth study of the main effects and interaction effects of the viticultural factor grapevine age and the oenological factors yeast starter culture and musts turbidity on the aroma of the studied wines. Main and interaction effects of all factors on the aroma of Riesling wines could be shown. For instance, the sensory impression of wines made from turbid musts of old and young grapevines were rated differently for two of the three yeast starter cultures. Different yeast starter cultures reacted differently to must turbidity, and this effect depended on the composition of the must (must from old grapevines vs. must from young vines). The multifactorial strategy used in this chapter shows how the effect of factors can correlate with each other, emphasising the importance and necessity of studying several possible factors at the same time.

Several general conclusions may be drawn from the results presented in this thesis.

Non-targeted GC-MS fingerprinting of wine result in a more holistic view on the composition of wine volatiles compared to targeted methods, which are always focused on a certain set of a priori known and identified compounds. Matrix algebra and advanced chemometric modelling are powerful tools for alternative approaches to non-targeted chromatographic data analysis. Problems concerning feature selection and retention time correction when using conventional approaches can be avoided by applying mathematical transformations on the raw data points of the chromatograms and subsequent modelling. The data analysis approaches presented here offer a useful alternative to conventional methods. The development of such approaches require an 'out of the box' thinking, considering chromatograms as signals from an instrument and not as a sequence of peaks which 'have to' be integrated to obtain useful data.

Programming skills are however necessary to implement algorithms.

Non-targeted GC-MS fingerprinting of wine provides comprehensive analytical data on the composition of the analysed wines. Drawing conclusions from the volatile composition of a wine to its sensory properties is not possible. The merging of data from sensory analysis and analysis of volatiles is therefore important. The presented possibility of combining data from the developed approach herein and projective mapping offers an effective tool to comprehensively study wine aroma by obtaining correlations between aroma descriptors and volatile compounds. The extension of this strategy to multifactorial experimental wine making contributed to significant new information regarding the main and interaction effects of grapevine age, yeast starter cultures and must turbidity on the aroma of Riesling wines.

The here developed data analysis approaches could be further adapted to other analytical techniques such as comprehensive two dimensional gas chromatography coupled to mass spectroscopy (GC×GC-MS) and liquid chromatography mass spectrometry (LC-MS). Moreover, the implementation into a software package including a graphical user interface would make this data analysis approach accessible to analytical scientist without programming experience. Considering more applications in viticultual and oenological studies, the usage of more advanced experimental designs or the combination of screening designs and full factorial designs could facilitate the inclusion of more oenological and viticultural factors into aroma studies.

  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Overlay of all mass channels of one sample (sample no. 14) of the artificial GC-MS data set. Dotted lines show the segmentation of the chromatograms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Overlay of TICs of all samples of the artificial GC-MS data set with introduced shift. Dotted lines show the segmentation of the chromatograms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 . 3

 53 PARAFAC loadings: component one vs. two. . . . . . . . . . . . . . 5.4 PARAFAC loadings: component one vs. seven. . . . . . . . . . . . . 5.5 PARAFAC loadings: component one vs. ten. . . . . . . . . . . . . . 5.6 PCA scores and loadings: PC1 vs. PC2. . . . . . . . . . . . . . . . 5.7 PCA scores and loadings: PC1 vs. PC3. . . . . . . . . . . . . . . . 5.8 PCA scores and loadings: PC1 vs. PC4. . . . . . . . . . . . . . . . 5.9 Boxplot of ethyl 2-hexenoate peak areas for each of the experimental wines. Wines are labeled as follows: OenfKN: Oenoferm Klosterneuburg, X5: Zymaflore X5, EC1118: EC1118, O: old vines, Y: young grapevines, T: turbid must, C: clear must. . . . . . . . . . . . . . . . . . . . . .

  as a powerful approach to simultaneously study the volatile composition and the sensory characteristics of experimental wines. This methodology was used to study the impact of different malolactic fermentation scenarios on two different Pinotage wine styles and for a full factorial investigation of the impact of grape vine age, must turbidity and yeast strain on the aroma of Riesling experimental wines. introductionWine has been an essential part of the sophisticated way of life in many cultures for thousands of years. From the ancient Egyptians to European cultures nowadays, wine consumption and production has, however, changed significantly. Compared to the ancient Retsina wines, which were also used as medicine, today's high quality wines are made to meet sensory expectations of the modern consumer. In other words, winemakers want to meet consumer preferences for distinct wine styles. As a consequence, the most important quality driver of modern wine is its aroma. The modulation of wine aroma presupposes a vast understanding of the volatile composition of wine and the impact of viticultural and oenological influencing factors.

  fingerprintings of wine volatiles provide important analytical data. It is however not possible to draw conclusions regarding the sensory properties of wines from analytical data alone. The linkage of analytical and sensory data is in this regard a important necessity in wine aroma research. A further aim of this thesis was therefore the integration of results from the developed GC-MS fingerprinting approach with rapid sensory screenings such as partial projective mapping to obtain a more holistic view on the aroma of wines. The combination of these two techniques provides an fast and efficient tool for multi-parametric aroma studies of experimental wines. Two relevant topics of interest in wine research have been addressed. The first application was the investigation of the impact of different malolactic fermentation (MLF) starter cultures and inoculation scenarios on the aroma expression of two different Pinotage styles. The second application comprised experimental wine making in full factorial design to study the effects of grapevine age, turbidity and yeast starter culture on the aroma of Riesling wines and how these factors influence each other.

Chapter 3 -

 3 Development of new approaches to non-targeted GC-MS data analysisChapter 3 constitutes the major part of the thesis and deals with the development of new approaches for non-targeted GC-MS data analysis which consider retention time shifts and avoid feature selection such as peak picking. After the background considerations on the used strategy are presented, both approaches are tested on an artificial and a real GC-MS data set and validated with an reference method. Chapter 4 -Application 1: Comparative aroma study on the impact of different malolactic fermentation scenarios on two Pinotage wine styles

  When multi-channel detectors are used, the chromatographic separation and the detector provide different dimensions of data. Different representations of a section of a two-dimensional GC-MS chromatogram are shown in Figure 2.1. A GC-MS chromatogram can be considered as a matrix of dimensions, scan number × mass channels. A data set of multiple two dimensional GC-MS chromatograms can consequently be represented as a three-way array (Figure 2.1(b)). Single channel detectors such as the Flame Ionisation Detector (FID) simply produce a time resolved signal similar to the Total Ion Chromatogram (TIC) of a GC-MS chromatogram, which represents the elution time profile of the summed MS dimension (Figure 2.1(c)).

  Three-way array: I samples × J scans (elution profile) × K mass channels

Figure 2 . 1 :

 21 Figure 2.1: Different representations of a two-dimensional GC-MS chromatogram section (peak system) consisting of 40 scans (time points) and 100 mass channels.

Figure 2 . 2 :

 22 Figure 2.2: Visualisation of the PARAFAC model for a GC-MS data set X with I samples × J scans (elution profile) × K mass channels; the loading matrices A, B, and C; R factors (components) and the residual array E.

Figure 2 . 3 :

 23 Figure 2.3: Visualisation of the PARAFAC2 model for a GC-MS data set X with I samples × J scans (elution profile) × K mass channels; the loading matrices A, B i , and C; R factors (components) and the residual array E.

A

  large extent of terpenols (including diols and triols) in grapes are bound as non-volatile glycosides and are therefore not aroma active. These glycosides mainly contain the monosaccharide β-D-glucose and the disaccharides α-L-arabinofuranoseβ-D-glucopyranose, α-L-rhamnopyranose-β-D-glucopyranose, β-D-xylopyranose-β-Dglucopyranose and β-D-apiofuranose-β-D-glucopyranose. Besides terpenols, other compounds with hydroxyl groups such as hexanol, 2-phenyl ethanol, benzyl alcohol, C13norisoprenoids and volatile phenols (e.g. vanillin) are present in glycosylated from.

  [START_REF] Castro | Saccharomyces cerevisiae Oxidative Response Evaluation by Cyclic Voltammetry and Gas Chromatography-Mass Spectrometry[END_REF] used among other techniques non-targeted HS-SPME-GC-MS analysis in combination with the software package MetAlign[START_REF] Lommen | MetAlign 3.0: performance enhancement by efficient use of advances in computer hardware[END_REF] to study the effect of oxidative response of Saccharomyces cerevisiae during fermentation.[START_REF] Castro | Application of a high-throughput process analytical technology metabolomics pipeline to Port wine forced ageing process[END_REF] developed a process analytical technology pipeline including the combination of GC-MS data preprocessing and multivariate analysis to investigate 'forced ageing' of Port wine. Another non-targeted study on volatiles related to port wine aging from[START_REF] Jacobson | Untangling the Chemistry of Port Wine Aging with the Use of GC-FID, Multivariate Statistics, and Network Reconstruction[END_REF] uses GC-FID, multivariate statistics and network reconstruction. Network reconstruction of preprocessed GC-MS data has also been used by[START_REF] Monforte | Chemiomics: Network Reconstruction and Kinetics of Port Wine Aging[END_REF] to study kinetics of port wine aging. A methodology by[START_REF] Schmidtke | Wine metabolomics: objective measures of sensory properties of semillon from GC-MS profiles[END_REF] uses multivariate curve resolution applied to GC-MS profiles coupled with full descriptive sensory analysis to determine the objective composition of various styles of Australian Semillon wines. Robinson et al. (2011a,b) developed a non-targeted method for characterizing the wine volatile profile using Headspace Solid Phase Microextraction Comprehensive Two-dimensional Gas Chromatography Time-of-flight Mass Spectrometry (HS-SPME-GC×GC-TOFMS) and studied the influence of yeast strain, canopy management, and site on the volatile composition and sensory attributes of Cabernet Sauvignon wines. A non-targeted strategy for the varietal authentication of German white wines based on Headspace Solid Phase Microextraction Gas Chromatography Mass Spectrometry (HS-SPME-GC-MS) and multivariate classification was published by Springer et al. (2014). Fedrizzi et al. (2012) introduced an optimization procedure for non-targeted HS-SPME-GC-TOF metabolite profiling of grape volatiles using Doptimal design.Howell et al. (2006) used a non-targeted GC-MS method to show that multiple strains of Saccharomyces grown together in grape juice can affect the profile of aroma compounds that accumulate during fermentation. Silva Ferreira et al. (2014) describe a non-invasive, high throughput GC-MS methodology facilitating 'real time' monitoring of the metabolic changes during fermentation of Saccharomyces cerevisiae in synthetic grape must containing different sources of yeast assimilable nitrogen. A study conducted by Conterno et al. (2013) used non-targeted and targeted metabolomic approaches to reveal compounds which characterise the growth of Dekkera bruxellensis in media with low nutrient availability and different ethanol concentrations modelling the wine environment. In a study on lactic acid bacteria, Lee et al. (2009) compared the metabolic profile of isolated Lactobacillus plantarum and commercial Oenococcus oeni using GC-MS and Nuclear Magnetic Resonance (NMR).

Figure 2 . 4 :

 24 Figure 2.4: Fictitious example of a projective mapping sheet of six red wines with freely chosen sensory descriptors from Ultra Flash Profiling.

Figure 3 . 1 :Figure 3 . 2 :

 3132 Figure 3.1: Overlay of all mass channels of one sample (sample no. 14) of the artificial GC-MS data set. Dotted lines show the segmentation of the chromatograms.

Figure 3 . 3 :

 33 Figure 3.3: Unfolding of the three-way array (i × j × k ), where i is the number of samples, j is the elution profile (number of scans) and k is the number of mass channels, into a new matrix (i × jk ).

Figure 3 .

 3 Figure 3.4 shows the scores and loading plots of a PCA on the autoscaled TICs

  10.6% expl. var. PC 2: 9.0% expl. var. (b) Loadings: PC 1 vs PC 2

Figure 3 . 4 :Figure 3 . 5 :

 3435 Figure 3.4: Scores and loadings plot of the first two principal components of the PCA (auto-scaled) on the TICs of the artificial data set (without peak shifts). Samples are coloured according to Table3.1.

Figure 3 . 6 :Figure 3 . 7 :

 3637 Figure 3.6: Scores and loadings plots of the first two principal components of the PCA (autoscaled) on the unfolded three-way array (Figure 3.3) of the artificial data set (without peak shifts). Samples are coloured according to Table 3.1.

  be less simple compared to a multi-way model such as Tucker3. With orthogonal factors Tucker3 is also known as multi-way PCA. The two component PCA model on the 20 × 311300 unfolded three-way array consists of 622640 parameters (2 × 20 + 2 × 311300), whereas a [2 2 2] component TUCKER model of the 20 × 1100 × 283 three-way array consists of 2814 parameters (2 × 20 + 2 × 1100 + 2 × 283).

2 - 3 Figure 3 . 8 :

 2338 Figure 3.8(c)). Component three represents the difference between the samples one to five and six to ten(Figure 3.8(b)). These samples are associated with peak number four, which negatively correlates with this component(Figure 3.8(c)). The loadings of the third mode which indicate the importance of the mass channels to each of the components are shown inFigure 3.8(d). These results show that the Tucker3 model is more appropriate than two-way PCA on the unfolded array to extract all relevant structural information out of the artificial GC-MS data set. Tucker3 is advantageous over the two-way approach, because the nature of the model corresponds to the nature of the data as has been discussed above (multi-way models for multi-way data).

Figure 3 .

 3 Figure 3.9 shows the loadings of a[3 3 3]-Tucker3 model on the artificial GC-MS data with introduced non-linear peak shifts for every peak. An overlay of all TICs of this shifted data set is displayed in Figure3.2. The loadings of the sample mode (mode one) of the[3 3 3]-Tucker3 model, presented in Figure3.9(a) (only component one vs. component two), show that all samples randomly scatter and no structural information on the different groups of samples is obtained. In Figure3.9(b), which shows the loadings of the second mode (elution profile) on component one to three, typical patterns for loadings of shifted peaks which look similar to the first derivative of a peak can be observed.

3 Figure 3 . 9 :

 339 Figure 3.9: Loadings of modes one to three of the Tucker3 model on the threeway array of the artificial GC-MS dataset with shifted peaks. Samples are coloured according to Table 3.1.

Figure 3 .

 3 Figure 3.10: Three simulated two dimensional GC-MS peaks consisting of scans (retention time) and 5 mass channels, represented as the matrices X, Y and Z, and their SSCP matrices XX T , Y Y T and ZZ T (modified from van Mispelaar et al. (2003)).

  number of mass channels × number of mass channels and contain information on the variation of each mass channel and covariation between all mass channels in each segment for the corresponding sample. For each segment the constructed SSCP matrices of all samples are vectorized and compiled into a new matrix. This step results in a compilation matrix for each segment with the dimensions number of samples × [(number of mass channels + 1) • number of mass channels / 2].These compilation matrices are then also transformed into SSCP matrices with the dimensions of number of samples × number of samples, which contain information about the variation of the content of the compilation matrix for each sample and the covariation of the content of the compilation matrix between all samples in each segment. These SSCP matrices are finally compiled in a three-way array with the dimension (number of samples × number of samples) × number of segments.

3 Figure 3 .Figure 3 .

 333 Figure 3.11: Loadings of the modes one and three of the PARAFAC model on the three-way array of the segmented and mathematically transformed artificial GC-MS dataset without noise and without shifted peaks. Note that mode one and two are identical. Samples are coloured according to Table3.1.

  kindly and freely available on www.chimiometrie.fr (July 2014). From the tested models only CCSWA gave interpretable results which are shown in Figure 3.13. A CCSWA model with 4 components revealed the structural information in the data (Figure 3.12) comparable to the results from PARAFAC (Figure 3.12). Common component one (90.8 % explained variance) separates the samples one to ten and eleven to 20, while segment five has the strongest influence on this component. Common component two (4.1 % explained variance) explains differences between the samples 14 and 15 and the other samples (Figure 3.13(a)). Segment two shows the highest weight on this component. The differences among the samples one to five from the other samples are explained by common component three (Figure 3.13(b)), on which segment two has a high salience value. Component four (Figure 3.13(c)) shows the same random variation reflecting noise in the data as component four of the previous PARAFAC model.

Figure 3 .

 3 Figure 3.14: Scores and loadings plots of the first two principal components of the PCA (autoscaled) on the final matrix Z of the artificial data set (without peak shifts).Samples are coloured according to Table3.1. Numbers in the loadings plots refer to the segment and the singular value of the segment (e.g. 1 2: segment 1, second singular value).

Figure 3 .

 3 Figure 3.15: Scores and loadings plots of the first two principal components of the PCA (class centroid centered and scaled to intra-class variance) on the final matrix Z of the artificial data set (without noise and peak shift). Samples are coloured according to Table3.1. Numbers in the loadings plots refer to the segment and the singular value of the segment (e.g. 1 2: segment 1, second singular value).

Figure 3 .

 3 Figure 3.16: Scores and loadings plots of principal components 2 and 3 of the PCA (class centroid centered and scaled to intra-class variance) on the final matrix Z of the artificial data set (without noise and peak shift). Samples are coloured according to Table3.1. Numbers in the loadings plots refer to the segment and the singular value of the segment (e.g. 1 2: segment 1, second singular value).

Figure 3 .

 3 Figure3.17: Scores and loadings plots of the first two principal components of the PCA (class centroid centered and scaled to intra-class variance) on the final matrix Z of the artificial data set (with noise and peak shift). Samples are coloured according to Table3.1. Numbers in the loadings plots refer to the segment and the singular value of the segment (e.g. 1 2: segment 1, second singular value).

Figure 3 .

 3 Figure 3.18: Scores and loadings plots of principal components 2 and 3 of the PCA (class centroid centered and scaled to intra-class variance) on the final matrix Z of the artificial data set (with noise and peak shift). Samples are coloured according to Table3.1. Numbers in the loadings plots refer to the segment and the singular value of the segment (e.g. 1 2: segment 1, second singular value).

For

  GC-MS analysis an Agilent 6890 GC coupled to a quadrupole mass spectrometer Agilent 5973 N (Agilent Technologies, Palo Alto, CA, USA) was used applying electron impact ionisation (EI) at 70 eV. Full mass spectra were acquired in the range 35 u to 300 u at an acquisition rate of four spectra per second. The ion source temperature was set to 230 • C, and the detector voltage was 2105 V. Separation was carried out on a 30 m HP-5 MS column with an internal diameter (i.d.) of 0.25 mm and a film thickness of 0.25 µm. The following oven temperature program was used: 40 • C; kept for 5 min; ramped at 15 • C min -1 to 250 • C; and held for 5 min, resulting in a total run time of 25 min. Thermal desorption and injection were performed using a split/splitless injector, operated at 250 • C in the splitless mode, with a splitless time of 3 min. Helium was used as carrier gas at a constant flow of 1.0 mL min -1 . Linear retention indices were calculated using a series of n-alkanes. Experimental retention indices were compared to literature values to confirm tentative peak identification based on mass spectra. All chromatographic analyses were performed in triplicate. 3.6.1.3 Data Treatment All raw chromatograms were exported from Agilent Chemstation version D.03.-00.611 as netCDF-files and imported into MATLAB version 8.0 (R2012b) (The Math-

Figure 3 .

 3 Figure 3.19: Loadings plots of PARAFAC components three vs. eleven (model with 71 segments); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

  17.6% expl. var. Component 2: 11.3% expl. var. (b) Third mode (segments) congruence loadings

Figure 3 .

 3 Figure 3.20: Loadings plots of PARAFAC components one vs. three (model with 71 segments); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

-

Figure 3 .

 3 Figure 3.21: Loadings plots of PARAFAC components one vs. four (model with 71 segments); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

  , 3.20 and 3.21). The remaining six components mainly reflect unsystematic variations in the chromatograms, for instance component five shown in Figure3.22. From the congruence loadings of the segment mode of this component in Figure3.22(b) it is evident that only one segment, that is segment 73, is responsible for the discrepancy of samples on this component (Figure 3.22(a)). The overlay of the TICs of segment 73 of all samples in Figure 3.22(c) shows that component 5 represents quantitative information in segment 73 very well. One injection of each of the wines made with the yeast/bacteria combination Lalvin Clos/Lalvin PN4 sequentially inoculated (clos PN4 seq) and the wine made with the yeast/bacteria combination Uvaferm RBS/O-Mega sequentially inoculated (rbs 271 seq) shows a much higher peak than all other samples in this segment. This pattern is exactly reproduced in the loadings of the sample mode of component 5. All other components containing redundant information are not further discussed here. PARAFAC components three and eleven are displayed in Figure 3.19(a) showing

Figure 3 .

 3 Figure 3.22: Loadings plots of PARAFAC components one vs. five (model with 71 segments); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

Figure 3 .

 3 Figure 3.20 shows the PARAFAC results for components one and two. Component one (17.6 % explained variation) mainly explains the differences in the wine fermented with the yeast Uvaferm RBS and the lactic acid bacteria O-Mega sequentially inoculated (rbs 271 seq), but this component also shows a general difference between co-inoculated and sequentially inoculated wines. Component two (11.3 % explained variation) mainly describes the distinction of the wine fermented with the yeast/bacteria combination Lalvin Clos/Enoferm Beta sequentially inoculated (clos beta) compared to all other wines. Congruence loadings of the segment mode for component one and two are shown in3.20(b).Segments 4, 6, 11, 18, 28, 31, 33, 35,

Figure 3 .

 3 Figure 3.23: Overlays of total ion chromatograms (TICs) of all 36 injections (including replicates) of the HS-SPME-GC-MS analysis of the 12 Cabernet Sauvignon wines (segment 64 to segment 73).

Figure 3 .

 3 Figure 3.24: Loadings plots of PARAFAC components one vs. two (model with 36 segments); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

Figure 3 .Figure 3 .

 33 Figure 3.25: Loadings plots of PARAFAC components one vs. three (model with 36 segments); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

  16.2% expl. var. Component 1: 19.1% expl. var. (b) Third mode (segments) congruence loadings

Figure 3 .

 3 Figure 3.27: Loadings plots of PARAFAC components two vs. one (model with 18 segments); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

  Figure 3.28: Loadings plots of PARAFAC components two vs. three (model with 18 segments); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

Figure 3 .Figure 3 .

 33 Figure 3.29: Scores and loadings plots of PC1 and PC2 of the PCA on the final matrix Z (Equation 3.16) of the Cabernet Sauvignon data set, where each of the twelve treatments were used as classes for class centroid centering and scaling to intra-class variance. Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

5 :Figure 3 .

 53 Figure 3.31: Scores and loadings plots of PC5 and PC6 of the PCA on the final matrix Z (Equation 3.16) of the Cabernet Sauvignon data set, where each of the twelve treatments were used as classes for class centroid centering and scaling to intra-class variance. Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

3 .Figure 3 .

 33 Figure 3.32: Scores and loadings plots of PC1 and PC2 of the PCA on the final matrix Z (Equation 3.16) of the Cabernet Sauvignon data set, where classes for class centroid centering and scaling to intra-class variance were defined according to the three yeast starter cultures. Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

  and 38 for component three, segment 41, 43, 51 and 63 for component four and segments 9 and 20 for component eleven. To confirm the results from PARAFAC modelling of the segmented and transformed GC-MS chromatograms and to study the important chromatogram segments which are responsible for the discrimination of samples in more detail, all of these 38 segments were deconvoluted using PARAFAC2 on each of the segments. The number of factors for each of the PARAFAC2 models were first evaluated as described by Johnsen et al. (2014) using the autochrom.m MATLAB function, which is kindly and freely provided on www.models.life.ku.dk (July 2014).

3. 6

 6 .6.1 PCA 1: PARAFAC components 3 and 11 All compounds in the segments which had high congruence loadings on the components three and eleven of the PARAFAC model with 71 segments (Figure3.19), which distinguished all samples according to the used yeast starter culture, were included in the first PCA. A two component PCA model was sufficient to separate the wines into three groups. The model was then improved by successively removing all compounds with low loadings on PC1 and PC2 (small impact on these two PCs). The wines fermented with the yeast starter culture Uvaferm RBS were separated from the other wines by PC1, explaining 67.4 % of the total variance (Figure3.33(a)). The loadings in Figure3.33(b) reveal that ethyl 2-methylbutyrate (1), isoamyl iso-butyrate (8), ethyl-2-hexenoate (15), the unknowns 46 and 49 (both terpenoid-like mass spectra) and the two unknowns 48 and 65 are positively correlated with the wines made with the yeast Uvaferm RBS. Moreover, the grouping of the wines fermented with yeast Uvaferm VRB is explained by PC2 (20.8 % explained variance). Citronellol (compound 39) and the unknown compound 31 are positively correlated on PC2 with these wines. 3.6.6.2 PCA 2: PARAFAC component 1 All compounds in the segments which had high congruence loadings on component one of the PARAFAC model with 71 segments (Figure 3.20) were included in 112

Figure 3 .

 3 Figure 3.33: Scores and loadings plots of the PCA of compounds in segments which had high congruence loadings on components three and eleven of the PARAFAC modell with 71 segments; Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

Figure 3 .

 3 Figure 3.34: Scores and loadings plots of the PCA of compounds in segments which had high congruence loadings on component one of the PARAFAC model with 71 segments; Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

Figure 3 .

 3 Figure 3.35: Scores and loadings plots of the PCA of compounds in segments which had high congruence loadings on components two and four of the PARAFAC model with 71 segments; Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

  (a) and 3.37(a)), albeit the interpretation remains difficult. PC1 shows, as component one from the PARAFAC model with 71 segments (Figure 3.20), a difference between most of the co-inoculated and sequentially inoculated wines. The co-inoculated wines fermented with the yeast starter culture Uvaferm RBS correlate most positively, while the wine made with the yeast starter culture

  and 146 show high positive loadings on PC1 (Figure 3.36(b)). These results are comparable to component one of the PARAFAC model with 71 segments (Figure 3.34). While the compounds 131, 137 and 139 correlate negatively with PC1, showing a similar pattern as reflected in PARAFAC component two of the 71 segment model (Figure 3.35). PC2 shows differences of the wines fermented with the yeast starter culture Uvaferm RBS and the wine made with the yeast/lactic acid bacteria combination Lalvin Clos/Lalvin PN4

Figure 3 .

 3 Figure 3.36: Scores and loadings plots of PC1 and PC2 of the PCA on all autoscaled compounds of all deconvoluted segments; Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

Figure 3 .

 3 38 shows the scores and loading of PC1 (38.8 % explained variance) and PC3 (10.7 % explained variance) of this PCA. The grouping according to yeast starter cultures are similar to the PCA on the autoscaled compounds of segments with high congruence loadings of component three and eleven of the PARAFAC model with 71 segments (Figure 3.33).Overall the results from the multiple PCAs after the PARAFAC2 deconvolution of 38 important segments from approach 1 and the results from PCA after PARAFAC2 modelling of all 71 segments are comparable, albeit the latter were more difficult to interpret and more sophisticated methods then PCA with autoscaling are needed, such as supervised preprocessing (class centroid centering and scaling to intra class 117

Figure 3 .

 3 Figure 3.37: Scores and loadings plots of PC1 and PC3 of the PCA on all autoscaled compounds of all deconvoluted segments; Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

Figure 3 .

 3 Figure 3.38: Scores and loadings plots of PC1 and PC3 of the PCA on all compounds of all deconvoluted segments, where class centroid centering and scaling by intra-class variance was applied; Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

  is characterized by volatile constituents, is one of the most important factors determining wine quality. The style of wine can be influenced by certain viticultural and oenological parameters, such as the harvesting date of grapes or different starter cultures for alcoholic and malolactic fermentation. Malolactic fermentation (MLF) is a crucial step particularly during the vinification of red wine.

  were carried out on an Agilent 6890 GC coupled to a quadrupole mass spectrometer Agilent 5973 N (Agilent Technologies, Palo Alto, CA, USA) using electron impact ionisation (EI) at 70 eV. Detector voltage and ion source temperature were set to 2105 V and 230 • C, respectively. Full mass spectra were acquired in the range from 35 u to 300 u at four spectra per second. For chromatographic separation a 30 m HP-5 MS column with an internal diameter (i.d.) of 0.25 mm and a film thickness of 0.25 µm was used. Thermal desorption and injection was done at 250 • C using a split/splitless injector in splitless mode, applying a splitless time of 3 min. The applied oven program was as follows: 40 • C; kept for 5 min; ramped at 15 • C min -1 to 250 • C; and held for 5 min. The total run time was 25 min. Helium was used as carrier gas at a constant flow of 1.0 mL min -1 . Linear retention indices were calculated using a series of n-alkanes. To confirm tentative peak identification based on mass spectra experimental retention indices were compared to literature values. All chromatographic analyses were performed in triplicate. 4.2.1.2 Data Treatment GC-MS chromatograms were exported from Agilent Chemstation version D.03.-00.611 (Agilent Technologies, Palo Alto, CA, USA) as netCDF-files and imported into MATLAB version 8.0 (R2012b) (The MathWorks Inc., Natick, MA, USA) us-

4. 2 . 4

 24 Partial projective mapping with free choice profilingSensory analysis was conducted in the same week of the GC-MS analysis. Partial projective mapping with free choice profiling (according to Ultra Flash Profiling as described by[START_REF] Perrin | Comparison of three sensory methods for use with the Napping® procedure: Case of ten wines from Loire valley[END_REF]) was performed with 18 wine experts from research laboratories of the Institut des Sciences de la Vigne et du Vin (ISVV), Bordeaux University. For orthonasal evaluation of the six experimental wines 50 mL of wine were presented in clear INAO wine glasses, which were labelled with random three-128 digit codes and covered with plastic Petri dishes. The tasting was conducted at room temperature in an ISO 8589:2007 certified degustation room equipped with a cubicle for each taster. All six wines were simultaneously presented in random order to the assessors, which were asked to position wines which they perceive as similar close to each other and wines which they perceive as different apart from each other on a 42.0 × 59.4 cm sheet of paper. Moreover, all assessors were encouraged to write aroma descriptors of their own choice next to each wine.

  11.5% expl. var. Component 5: 5.8% expl. var. (b) Third mode (segments) congruence loadings

Figure 4 .

 4 Figure 4.1: PARAFAC loadings: component one vs. five. Numbers in (b) correspond to the segment number.

  11.5% expl. var. Component 6: 4.3% expl. var. (b) Third mode (segments) congruence loadings

Figure 4 . 2 :

 42 Figure 4.2: PARAFAC loadings: component one vs. six. Numbers in (b) correspond to the segment number.

Figure 4 . 3 :

 43 Figure 4.3: Results of MFA of partial projective mapping (orthonasal evaluation only), where frequencies of aroma descriptor groups of the free choice profiling were included as categorical supplementary variables (c) and peak areas (autoscaled) as continuous supplementary variables (b). Wines in (a) are labeled as follows: early harvested: EH (green), late harvested: LH (red), Lalvin PN4: PN4, Lalvin VP41: VP41, Lalvin V22: V22, co-inoculation: coin, sequential inoculation: seq. Numbers in (c) correspond to integrated compounds in Table4.2.

Figure 4 . 4 :

 44 Figure 4.4: (a) PARAFAC loadings of the sample mode (component 1 vs. 6) with superimposed rotated MFA scores (grey) of GPA. For MFA of the partial projective mapping the frequencies of aroma descriptors of the free choice profiling were included as categorical supplementary variables (c). Wines in (a) are labeled as follows: early harvested: EH (green), late harvested: LH (red), Lalvin PN4: PN4, Lalvin VP41: VP41, Lalvin V22: V22, co-inoculation: coin, sequential inoculation: seq. Numbers in (b) correspond to chromatogram segments in Table4.2.

2183a

  For each segment only compounds showing significantly different peak area values between treatments are listed. b Experimentally determined linear retention indices.

  analyses were performed on an Agilent 6890 GC coupled to an Agilent 5970 N quadrupole mass spectrometer (Agilent Technologies, Palo Alto, CA, USA) operated in electron impact ionisation (EI) mode at 70 eV. A detector voltage of 2010 V was used and the ion source temperature was set to 230 • C. Full mass spectra were acquired in the range of 35 u to 350 u. For thermal desorption and injection a split/splitless injector operated at 250 • C with a splitless time of 3 min was used. Chromatographic separation was performed on a 30 m HP-5 MS column with an internal diameter (i.d.) of 0.25 mm and a film thickness of 0.25 µm. To guarantee a fast separation the GC oven temperature program was chosen as follows: 40 • C, kept for 5 min; ramped at 15 • C min -1 to 250 • C; and held for 5 min resulting in a total run time of 25 min. Helium was used as carrier gas at constant flow of 1.0 mL min -1 .

5. 2 . 4

 24 GC-MS fingerprinting: Segmentation, mathematical transformation and PARAFAC modelling of GC-MS chromatogramsThe data analysis approach 1 which has been described in Section 3.4 has been used here for GC-MS data analysis. The approach is summarized as follows: The initial step consists of the examination of overlays of total ion chromatograms (TICs) of all samples and the segmentation of the chromatograms (retention time profile × mass spectral dimension) along the retention time profile. Subsequently, all twodimensional chromatogram segments of all samples are transformed to SSCP matrices in a way that the retention profile is eliminated. For each segment the upper triangular part of the obtained SSCP matrices are vectorized and concatenated into a compilation matrix. Subsequently, each of the compilation matrices are transformed into SSCP matrices, which are finally assembled into a three-way array of the size number of samples × number of samples × number of segments. The obtained threeway array is analysed using PARAFAC to find differences among samples and the corresponding chromatogram segments responsible for the discrimination of samples.

Figure 5 . 1 :

 51 Figure 5.1: Fermentation kinetics. Wines are labeled as follows: OenfKN: Oenoferm Klosterneuburg, X5: Zymaflore X5, EC1118: EC1118.

Figure 5 . 2 :

 52 Figure 5.2: MLR coefficients according to the model postulated in Equation5.1. Factor X 1 : age of vines, factor X 2 : must turbidity, factor X 3 : yeast strain (see Table5.1 for further details on the factorial design). Response variables are the PARAFAC loadings of the sample mode of each of the components. Significance is indicated as follows: p > 0.05 *, p > 0.01 **, p > 0.001 ***. Adjusted R 2 = adj R 2

Figure 5 . 3 :

 53 Figure 5.3: PARAFAC loadings: component one vs. two.

Figure 5 . 4 :

 54 Figure 5.4: PARAFAC loadings: component one vs. seven.

Figure 5 . 5 :

 55 Figure 5.5: PARAFAC loadings: component one vs. ten.

Figures 5 .

 5 Figures 5.3(b), 5.4(b) and 5.5(b) show the importance of each segment on the corresponding PARAFAC components. Segments 1,3, 4, 6, 7, 11, 12, 15, 16, 19, 20,

Figure 5 .

 5 Figure 5.7: PCA scores and loadings: PC1 vs. PC3.

Figure 5 . 8 :

 58 Figure 5.8: PCA scores and loadings: PC1 vs. PC4.

Figure 5 .Figure 5 . 9 :

 559 Figure 5.9: Boxplot of ethyl 2-hexenoate peak areas for each of the experimental wines. Wines are labeled as follows: OenfKN: Oenoferm Klosterneuburg, X5: Zymaflore X5, EC1118: EC1118, O: old vines, Y: young grapevines, T: turbid must, C: clear must.

  .2% expl. var. PC2: 13.6% expl. var.

  Figure 5.10: Results of MFA of the partial projective mapping (orthonasal evaluation only). Wines in (a) are labeled as follows: OenfKN: Oenoferm Klosterneuburg, X5: Zymaflore X5, EC18: EC1118, O: old vines, Y: young wines, T: turbid must, C: clear must. Frequencies of the aroma descriptor groups of the free choice profiling (b) were included as categorical supplementary variables. Autoscaled peak areas (c, numbers correspond to compounds in Table5.3) and data from FT-IR (d) were included as continuous supplementary variables.

Figure A. 1 :

 1 Figure A.1: Schematic representation of approach 1. Matrix indices were omited. Note that only the upper triangular matrices of all XX T are used.

  Figure B.2: Loadings plots of PARAFAC components one vs. four, where class centroid centering and scaling to intra-class variance was applied to the compilation matrix Y k (Equation 3.9); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

Figure B. 3 :

 3 Figure B.3: Loadings plots of PARAFAC components one vs. five, where class centroid centering and scaling to intra-class variance was applied to the compilation matrix Y k (Equation 3.9); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

  Figure B.4: Loadings plots of PARAFAC components seven vs. nine, where class centroid centering and scaling to intraclass variance was applied to the compilation matrix Y k (Equation 3.9); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271).

  I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Objectives of this study . . . . . . . . . . . . . . . . . . . . . 3 1.3 Thesis outline . . . . . . . . . . . . . . . . . . . . . . . . . . 4 II. Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . .

	Figure 2.1	2.1 Conventional targeted chromatographic data analysis . . . . . 2.2 Non-targeted and multivariate chromatographic data analysis 2.2.1 Chromatographic data structure . . . . . . . . . . . 2.2.2 Conventional non-targeted chromatographic data anal-ysis . . . . . . . . . . . . . . . . . . . . . . . . . . . Different representations of a two-dimensional GC-MS chromatogram section (peak system) consisting of 40 scans (time points) and 100 2.2.LIST OF FIGURES mass channels.	6 7 8 9 9

1 1.1 General introduction . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 3 Global multivariate modelling of chromatograms: Fingerprinting . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.4 Local multivariate modelling of chromatograms: Resolution of peaks (Deconvolution) . . . . . . . . . . . 17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Visualisation of the PARAFAC model for a GC-MS data set X with I samples × J scans (elution profile) × K mass channels; the loading matrices A, B, and C; R factors (components) and the residual array E. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Visualisation of the PARAFAC2 model for a GC-MS data set X with I samples × J scans (elution profile) × K mass channels; the loading matrices A, B i , and C; R factors (components) and the residual array E. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4

  Overlays of total ion chromatograms (TICs) of all 36 injections (including replicates) of the HS-SPME-GC-MS analysis of the 12 Cabernet Sauvignon wines (segment 64 to segment 73). . . . . . . . . . .

	3.23
	3.24	Loadings plots of PARAFAC components one vs. two (model with
		36 segments); Yeast starter cultures: Lalvin Clos (clos), Uvaferm
		RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures:
		Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4),
		Lalvin VP41 (41) and O-Mega (271). . . . . . . . . . . . . . . . . .
	3.25	Loadings plots of PARAFAC components one vs. three (model with
		36 segments); Yeast starter cultures: Lalvin Clos (clos), Uvaferm
		RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures:
		Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4),
		Lalvin VP41 (41) and O-Mega (271). . . . . . . . . . . . . . . . . .
	3.26	Loadings plots of PARAFAC components five vs. ten (model with
		36 segments)
	3.20	Loadings plots of PARAFAC components one vs. three (model with
		71 segments); Yeast starter cultures: Lalvin Clos (clos), Uvaferm
		RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures:
		Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4),
		Lalvin VP41 (41) and O-Mega (271). . . . . . . . . . . . . . . . . .
	3.21	Loadings plots of PARAFAC components one vs. four (model with
		71 segments); Yeast starter cultures: Lalvin Clos (clos), Uvaferm
		RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures:
		Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4),
		Lalvin VP41 (41) and O-Mega (271). . . . . . . . . . . . . . . . . .
	3.22	Loadings plots of PARAFAC components one vs. five (model with
		71 segments); Yeast starter cultures: Lalvin Clos (clos), Uvaferm
		ix

. . . . . . . . . . . . . . . . . . RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271). . . . . . . . . . . . . . . . . . ; Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271). . . . . . . . . . . . . . . . . . 3.27 Loadings plots of PARAFAC components two vs. one (model with 18 segments); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271). . . . . . . . . . . . . . . . . . 3.28 Loadings plots of PARAFAC components two vs. three (model with 18 segments); Yeast starter cultures: Lalvin Clos (clos), Uvaferm RBS (rbs), Uvaferm VRB (vrb); Lactic acid bacteria starter cultures: Enoferm Alpha (alpha), Enoferm Beta (beta), Lalvin PN4 (PN4), Lalvin VP41 (41) and O-Mega (271). . . . . . . . . . . . . . . . . . 3.29

  .1 PARAFAC loadings: component one vs. five. Numbers in (b) correspond to the segment number. . . . . . . . . . . . . . . . . . . . . . 4.2 PARAFAC loadings: component one vs. six. Numbers in (b) correspond to the segment number. . . . . . . . . . . . . . . . . . . . . . 4.3Results of MFA of partial projective mapping (orthonasal evaluation only), where frequencies of aroma descriptor groups of the free choice profiling were included as categorical supplementary variables (c) and peak areas (autoscaled) as continuous supplementary variables (b).

Wines in (a) are labeled as follows: early harvested: EH (green), late harvested: LH (red), Lalvin PN4: PN4, Lalvin VP41: VP41, Lalvin V22: V22, co-inoculation: coin, sequential inoculation: seq. Numbers in (c) correspond to integrated compounds in Table

4

.2. . 4.4 (a) PARAFAC loadings of the sample mode (component 1 vs. 6) with superimposed rotated MFA scores (grey) of GPA. For MFA of the partial projective mapping the frequencies of aroma descriptors of the free choice profiling were included as categorical supplementary variables (c). Wines in (a) are labeled as follows: early harvested: EH (green), late harvested: LH (red), Lalvin PN4: PN4, Lalvin VP41: VP41, Lalvin V22: V22, co-inoculation: coin, sequential inoculation: seq. Numbers in (b) correspond to chromatogram segments in Table 4.2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Fermentation kinetics. Wines are labeled as follows: OenfKN: Oenoferm Klosterneuburg, X5: Zymaflore X5, EC1118: EC1118. . . . . . . . . 5.2

Table 3 .

 3 1 Differing peaks (No. 2, 4 and 9) among samples in the defined, artificial GC-MS data set. All other peaks are of the same size in all samples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2

  3) on PARAFAC components one, two, seven and ten. . . . . . . . . . Schematic representation of approach 1 . . . . . . . . . . . . . . . . . Sibylle Krieger-Weber, Gilles de Revel, Doris Rauhut, André de Villiers. Toward Automated Chromatographic Fingerprinting: A Non-Alignment Approach to Gas Chromatrography Mass Spectrometry Data. Analytica Chimica Acta, 911, 42-58 Jochen Vestner; Célia Barril; Chantal Maury; Paul D. Prenzler; Geoffrey R. Scollary. The influence of stereochemistry of antioxidants and flavanols on oxidation processes in a model wine system: ascorbic acid, erythorbic acid, (+)-catechin and (-)-epicatechin. Journal of Agricultural and Food Chemistry, 58, 1004-11 Jochen Vestner; Stefanie Fritsch; Doris Rauhut. Development of a microwave assisted extraction method for the analysis of 2,4,6-trichloroanisole in cork stoppers by SIDA-SBSE-GC-MS. Analytica Chimica Acta, 660, 76-80

	xiv LIST OF APPENDICES CA Correspondence Analysis Appendix CCSWA Common Components and Specific Weights Analysis EFA Evolving Factor Analysis FID Flame Ionisation Detector GC Gas Chromatography GC-MS Gas Chromatography Mass Spectrometry GPA General Procrustes Analysis HCA Hierarchical Cluster Analysis HS-SPME-GC-MS Headspace Solid Phase Microextraction Gas Chromatography Mass Spectrometry HS-SPME-GC×GC-TOFMS Headspace Solid Phase Microextraction Compre-hensive Two-dimensional Gas Chromatography Time-of-flight Mass Spectrom-etry LAB Lactic Acid Bacteria LC-MS Liquid Chromatography Mass Spectrometry LLE Liquid-Liquid Extraction MCR Multivariate Curve Resolution MDS Multidimensional Scaling MFA Multiple Factor Analysis xvi PLS-DA Partial Least Squares Discriminant Analysis QDA Quantitative Descriptive Analysis SIMCA Soft Independent Modeling of Class Analogy SPE Solid Phase Extraction SPME Solid Phase Microextraction SSCP Sums of Squares and Cross Products SVD Singular Value Decomposition TIC Total Ion Chromatogram xvii 2016 Jochen Vestner, Other publications 2015 Chandré M. Willemse, Maria A. Stander, Jochen Vestner, André de Villiers. Comprehensive two-dimensional HILIC×RP-LC-UV-MS analysis of anthocyanins and derived pigments in red wine. Analytical Chemistry, 87 (24), 12006-12015 2013 Kathithileni M. Kalili; Jochen Vestner; Maria A. Stander; André de Villiers. Toward Unraveling Grape Tannin Composition: Application of Online Hydrophilic Interaction Chromatography × Reversed-Phase Liquid ChromatographyTime-of-Flight Mass Spectrometry for Grape Seed Anal-ysis. Analytical Chemistry, 85 (19), 9107-9115 2011 Jochen Vestner; Sulette Malherbe; Maret Du Toit; Hélène H. Nieu-woudt; Ahmed Mostafa; Tadeusz Górecki; Andreas G. J. Tredoux; André de Villiers. Investigation of the volatile composition of Pinotage wines fer-mented with different malolactic starter cultures using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrom-etry (GC×GC-TOF-MS). Journal of Agricultural and Food Chemistry, 59, 12732-12744 xviii (Oral) 10th International Symposium of Enology (OENO2015), Bordeaux, France (Oral) 36th Congress of the South African Society for Enology and Viticulture (SASEV), Stellenbosch, South Africa (Poster) 8th In Vino Analytica Scientia Symposium, Reims, France (Oral) 34th Congress of the South African Society for Enology and Viticulture (SASEV), Stellenbosch, South Africa (Poster) Hyphenated Techniques for Chromatography (HTC-12) & Hyphenated Techniques for Sample Preparation (HTSP-2), Bruges, Belgium (Poster) 36th International Symposium on Capillary Chromatography (ISCC) and 9th GCxGC Symposium, Riva, Italy (Poster) Analitika, Stellenbosch, South Africa (Poster) 32th Congress of the South African Society for Enology and Viticulture (SASEV), Stellenbosch, South Africa (Poster) xix A. xv LIST OF ABBREVIATIONS ALS Alternating Least Squares 9th In Vino Analytica Scientia Symposium (IVAS2015), Trentino, Italy AMDIS Automated Mass Spectral Deconvolution and Identification System MLF Malolactic Fermentation N-PLS Multi-way Partial Least Squares NMR Nuclear Magnetic Resonance OPLS-DA Orthogonal Partial Least Squares Discriminant Analysis PARAFAC Parallel Factor Analysis PARAFAC2 Parallel Factor Analysis 2 PC Principal Component PCA Principal Component Analysis PLS Partial Least Squares SCIENTIFIC COMMUNICATION ABSTRACT New Chemometric Approaches to Non-targeted GC-MS Fingerprinting Analysis of Wine Volatiles by Andrew C. Clark; Oral and poster communications Publications related to the subject of this thesis Jochen Vestner

B. Approach 1 with class centroid centering and scaling to intra-class variance applied to the compilation matrices Y k (Equation 3.9 in Section 3.6) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. MATLAB code approach 1 . . . . . . . . . . . . . . . . . . . . . . . . D. MATLAB code approach 2 . . . . . . . . . . . . . . . . . . . . . . . .

  The final step of conventional non-targeted approaches is the explorative multivariate data analysis of the obtained peak table to reveal systematic structure and

patterns in the data. Often univariate and multivariate statistical methods are used complementarily. T -tests, ANOVA or Fisher ratios

[START_REF] Pierce | Fisher ratio method applied to third-order separation data to identify significant chemical components of metabolite extracts[END_REF]

are examples for univariate methods, which can be used to explore different levels of individual compounds across two or multiple groups of samples. The information from these univariate tests can for instance be used for variable selection prior to multivariate modelling. Multivariate approaches can be divided into supervised techniques, where classification groups are defined in advance, and unsupervised techniques, where classification groups are not known or can not be defined in advance.

Principal Component Analysis (PCA) is by far the most commonly used unsupervised method. PCA is a projection technique that searches for common patterns in a data matrix (e.g. peak table) to establish new directions explaining variance in the original data cloud. Onto these directions, called the loadings, each sample can be projected. These projections are called scores. A set of scores and loadings is a principle component (also called latent variable). The first principal component explains most of the variation in the data, while the explained variation decreases with the number of further principle components. PCA is often used to obtain a initial overview, as it can reveal unknown grouping of samples or confirm suspected groupings of samples. Another common unsupervised technique is Hierarchical Cluster Analysis (HCA). HCA first defines a clusters for each sample and successively clusters samples together based on similarity measures until all samples constitute one cluster. The arrangement of the clusters (similarities among samples) are finally illustrated in a dendrogram (tree diagram)

Table 3 .

 3 1: Differing peaks (No. 2, 4 and 9) among samples in the defined, artificial GC-MS data set. All other peaks are of the same size in all samples.

	segment peak no. size difference sample no.
	1	2	only present in	14 & 15
	2	4	0.7× higher in	1 to 5
	5	9	3× higher in	1 to 10

To simulate baseline noise a random normal distributed noise was added to the whole data set. Each chromatogram can be considered as a matrix of dimensions 1100 scans × 283 masses, thus the entire data set can be considered as a three-way array (i × j × k ), with the dimensions 20 samples × 1100 scans × 283 masses.

Table 3

 3

	.1.

Table 3 .

 3 3: Cabernet Sauvignon wines. Sequential: lactic acid bacteria inoculation after completion of alcoholic fermentation; co-inoculation: lactic acid bacteria inoculation 24 h after yeast inoculation; LAB: lactic acid bacteria.

	No.	Inoculation mode	Yeast starter culture	LAB starter culture	Abbreviation
	1	co-inoculation	Lalvin Clos	Enoferm Alpha clos alpha coin
	2	sequential	Lalvin Clos	Enoferm Alpha clos alpha seq
	3	co-inoculation	Lalvin Clos	Enoferm Beta	clos beta coin
	4	sequential	Lalvin Clos	Enoferm Beta	clos beta seq
	5	co-inoculation	Lalvin Clos	Lalvin PN4	clos PN4 coin
	6	sequential	Lalvin Clos	Lalvin PN4	clos PN4 seq
	7	co-inoculation Uvaferm RBS	Lalvin VP41	rbs VP41 coin
	8	sequential	Uvaferm RBS	Lalvin VP41	rbs VP41 seq
	9	co-inoculation Uvaferm RBS	O-Mega	rbs 271 coin
	10	sequential	Uvaferm RBS	O-Mega	rbs 271 seq
	11 co-inoculation Uvaferm VRB Enoferm Alpha vrb alpha coin
	12	sequential	Uvaferm VRB Enoferm Alpha vrb alpha seq

  .20

						Abundance
		0	2	4		6	8 Abundance	10	12	14	16 x 10 4
		21.4		21.3	2	4	6	8	10	12	14	16 x 10	5
												segment 64
				21.35						segment 64
				21.4
		21.6		21.45 21.5					rbs 41 seq	rbs 271 seq		clos beta seq	segment 65
												segment 66
		21.8
				rbs 271 coin					segment 67
		22
					clos beta seq
	Retention time [min]	22.2								segment 68 segment 69 clos beta seq
												segment 70
		22.4				clos beta seq			segment 71
		22.6										segment 72
				clos PN4 seq
		22.8		rbs 271 seq						segment 73

Table 3 .

 3 4: Summary of all segments showing high congruence loadings (> 0.5) on PARAFAC components one, two, three, four and eleven and details on the PARAFAC2 models of each segment with corresponding compounds.

		congruence loadings
	of PARAFAC component
	segment 1	2	3	4	11	PARAFAC2 component no	no. compound name	LRI a MS match
			0.85			1	1 butanoic acid, 2-methyl-, ethyl ester	857	900
							(ethyl 2-methylbutyrate)
						2	2 butanoic acid, 3-methyl-, ethyl ester	861	852
							(ethyl 3-methylbutanoate)
						3	-baseline
	0.51		0.69			1	7 acetic acid, hexyl ester (hexyl	1005	931
							acetate)
						2	8 propanoic acid, 3-methyl-, ethyl	1003	812
							ester (iso-amyl iso-butyrate)
						3	9 unknown m/z(%) = 69(100), 68(53),	999
							142(32), 88(16), 97(12), 96(10)
	0.66					1	12 unknown m/z(%) = 57(100), 41(33), 43(27),	1022
							55(256), 70(242), 83(240), 56(215), 69(11)
						2	-baseline
						3	13 eucalyptol (1,8-cineole)	1025	877
			0.97			1	15 2-hexenoic acid, ethyl ester	1048
							(ethyl-2-hexenoate)

a experimentally determined linear retention indices

Table 4

 4

	.1: Pinotage wines. Sequential: lactic acid bacteria inoculation after com-
	pletion of alcoholic fermentation; co-inoculation: lactic acid bacteria inoculation 24 h
	after yeast inoculation; LAB: lactic acid bacteria.
	No.	Harvesting time	Inoculation mode	LAB starter culture	Abbreviation
	1	early	co-inoculation	Lalvin VP41	EH VP41 coin
	2	early	co-inoculation	Lalvin V22	EH V22 coin
	3	early	sequential	Lalvin VP41	EH VP41 seq
	4	late	co-inoculation	Lalvin PN4	LH PN4 coin
	5	late	sequential	Lalvin PN4	LH PN4 seq
	6	late	sequential	Lalvin VP41	LH VP41 seq

Table 4 .

 4 2: Summary of all segments and their corresponding tentatively identified compounds showing high loadings (congruence loadings > 0.5) on PARAFAC components one, five and six.

		congruence loadings
			of PARAFAC
			component
	segment 1	5	6	no. compound name a	LRI b	MS match
	1	0.61	0.94		1 1-butanol, 3-methyl acetate (iso-amyl	815	969
					acetate)
	2	0.86	0.73		2 3-hexenoic acid, ethyl ester	998	892
	3		0.9		3 acetic acid, hexyl ester		932
	4			0.94	4 unknown m/z(%) = 93(100), 68(97), 67(88),
					79(76), 94(56), 13(52), 92(51), 121(34)
	5	0.87	0.51	0.73	5 2-hexenoic acid, ethyl ester		839
	6	0.54			6 unknown m/z(%) = 70(100), 87(97), 43(93),
					71(88), 88(55), 102(47), 41(46), 55(36)
					7 ethyl 2-hydroxyhexanoate		840
	7	0.59			8 1-octanol		909
	11	0.66			9 unknown m/z(%) = 57(100), 81(52), 67(40),
					56(39), 55(38), 41(30), 82(30), 83(28)
	13	0.82	0.69		10 acetic acid, 2-phenylethyl ester		955
					(phenylethyl acetate)
	14	0.69	0.68		11 hexanoic acid, 3-methylbutyl ester
					(iso-amyl hexanoate)

Table 5

 5 SO 2 were added. Different Nephelometric Turbidity Unit (NTU) values of 300 and < 10 were adjusted with residual solids from must clarification after sedimentation for 24 h at 17 • C. Subsequently, 600 mL grape musts were aliquoted into 750 mL bottles and inoculated with the commercially available Saccharomyces cerevisiae yeast strains Oenoferm Klosterneuburg (Erbslöh Geisenheim AG, Geisenheim, Germany) and Zymaflore X5 (Laffort, Bordeaux, France) and the Saccharomyces bayanus EC 1118 (Lallemand Inc., Canada) according to manufacturer's instructions. These yeast starter cultures were chosen because of different aromatic properties as stated in their product data sheets and from own experience.Bottles were closed with air locks before fermentation. All fermentation treatments were done in quadruplicate. Alcoholic fermentation took place at 17 • C and was monitored by determining loss of carbon dioxide. After fermentations were completed the wines were stored at 4 • C for one week and racked. Free SO 2 levels were adjusted to 50 mg L -1 during racking using potassium bisulfite. All wines were stored at 4

	.1: Structure of the 2×2×3 full factorial design used for the experimental wine
	making.
	Factor	Level 1		Level 2	Level 3
		uncoded	coded uncoded coded uncoded coded
	X 1 : age of grapevines young	-1	old	+1
	X 2 : must turbidity	clear	-1	turbid	+1
	X 3 : yeast strain	Oenoferm KN (1 0) X5	(0 1) EC1118 (0 0)
	The grape must obtained from the two viticultural parcels (old grapevines 42
	years; young grapevines 18 years) were pressed under the same conditions regarding
	press load and pressing program. Each of the two musts were divided into two lots
	for clarification and 30 mg L -1

• C prior to chemical analysis. The common wine parameters glucose, fructose, fermentable sugars, density, tartaric acid, malic acid, total acidity, sugar free extract and glycerol were determined by FT-IR using a FOSS FT2 Winescan (FOSS Analytical A/S, Hillerød Denmark).

Table 5 .

 5 1 for further details on the factorial design). Response variables are the PARAFAC loadings of the sample mode of each of the components. Significance is indicated as follows: p > 0.05 *, p > 0.01 **, p > 0.001 ***. Adjusted R 2 = adj R 2

		0.3								1						11
							EC1118 X5			0.9			4		16
	Component 2: 12.2 % expl. var.	-0.2 -0.1 0 0.1 0.2			EC1118	old vs. young vines EC1118 + X5	Oenoferm KN turbid clear young vines old vines	Component 2: 12.2 % expl. var.	0.2 0.3 0.4 0.5 0.6 0.7 0.8		27 7 23	26	18			19						15	6
		-0.3 -0.3	-0.2	-0.1	0	0.1	0.2	0.3		0 0.1	0	5 8 9 10 14	0.1	0.2 12 28	0.3	24	0.4	0.5	20	0.6	1	0.7	0.8	0.9 3	1
				Component 1: 13.2 % expl. var.										Component 1: 13.2 % expl. var.
		(a) First mode (samples) loadings		(b) Third mode (segments) congruence
									loadings

  . The wines made from turbid must fermented with the yeast Oenoferm Klosterneuburg are separated from the other

											1			12
		0.3							EC1118 X5		0.9
									Oenoferm KN
	Component 10: 4.6 % expl. var.	-0.2 -0.1 0 0.1 0.2			EC1118 + Oenoferm	X5	EC1118		turbid clear young vines old vines Oenoferm	Component 10: 4.6 % expl. var.	0.2 0.3 0.4 0.5 0.6 0.7 0.8				24	20	1	15
		-0.3	-0.3	-0.2	-0.1 Component 1: 13.2 % expl. var. 0 0.1	0.2	0.3		0 0.1	0	0.1 7 4 23 14 8 5 9 10 27	0.2 18 28 26 16	0.3 11 Component 1: 13.2 % expl. var. 0.4 0.5 0.6 0.7 19	0.8	0.9 3	6	1
		(a) First mode (samples) loadings	(b) Third mode (segments) congruence
										loadings

  Conversely, the wines made from clear musts correlate negatively with this PC. The acetate esters ethyl acetate (compound 1) and hexyl acetate (compound 14), the unsaturated ester ethyl 9-decenoate (compound 27), the sesquiterpene nerolidol (compound 30) and the unknown compound 24 show negative loadings on PC1. According to the information revealed from component 2 of the PARAFAC model, the second PC of the PCA (19.9 % explained variance) relates mainly to the wines fermented with the yeast EC1118 (Figure5.6). The wines obtained from clear musts of old and young grapevines fermented with this yeast correlate highly with the fatty acid esters ethyl butanoate (compound 6), ethyl hexanoate (compound 13), ethyl octanoate (compound 23), methyl octanoate (compound 18), ethyl decanoate (compound 28), m-cymene (compound 16), the acetate ester isoamyl acetate and octanoic acid(compound 22). The wines made from turbid must of old grapevines fermented with the yeast EC1118 correlate with the esters from branched alcohols such as isobutyl acetate (compound 7), isobutyl hexanoate (compound 20), isopentyl

		8 6							EC1118 X5 Oenoferm KN turbid			0.3		13 18 23 22	6	25
	PC 2: 19.9% expl. var.	-2 0 2 4			EC1118		old vs. young vines EC1118	slightly turbid young vines old vines OenofermKN	PC 2: 19.9% expl. var.	0.2 -0.1 0 0.1	1 14 24 27	28 30	11 16	10	7	29 12 2 3 4 5 8 9 20 21 32 15 19 26
		-4										-0.2		31	17
		-6	OenofermKN & X5
												-0.3
		-8 -8	-6	-4	-2	0	2	4	6	8		-0.3	-0.2		-0.1	0	0.1	0.2	0.3
					PC 1: 52% expl. var.								PC 1: 52% expl. var.
				(a) scores								(b) loadings
				Figure 5.6: PCA scores and loadings: PC1 vs. PC2.
	(compound 19), the branched fatty acid esters ethyl isobutyrate (compound 5), ethyl
	2-methylbutanoate (compound 8), ethyl 3-methylbutanoate (compound 9), isobutyl
	hexanoate (compound 20), isopentyl hexanoate (compound 25), isoamyl octanoate
	(compound 29), isoamyl pentadecanoate (compound 32), and the two unknown com-
	pounds 12 and 26.
									, isoamyl alcohol (compound 4), 2-phenylethanol
										158

Table 5 .

 5 Segment 17 was excluded from the PARAFAC model due to interfering signal in this segment.

	3 -continued
		congruence loadings
			of PARAFAC
			component
	segment	1	2	7	10 no. compound name a	LRI b MS match
	12				1.00 15 unknown m/z(%) = 121(100), 93(85), 136(74),	863
					91(44), 105(17), 107(11), 103(10)
					16 m-cymene (1-isopropyl-3-methylbenzene)	811
					17 ethyl 2-hexenoate (ethyl	901
					(E)-hex-2-enoate)
	15	0.90			18 methyl octanoate	952
					19 2-phenylethanol	958
					20 isobutyl hexanoate (2-methylpropyl	845
					hexanoate)
	16		0.91 0.45	21 unknown m/z(%) = 101(100), 129(76), 128(19),
					102(13), 55(10), 73(8)
					22 octanoic acid	906
					23 ethyl octanoate	931
					24 unknown m/z(%) = 59(100), 93(95), 121(80),
					136(80), 81(58), 43(34), 92(32), 95(20)
	17 c				25 isopentyl hexanoate (3-methylbutyl	882
					hexanoate)
					26 unknown m/z(%) =99(100), 163(38), 117(10),
					105(9)
	19	0.41 0.66 0.38	27 ethyl 9-decenoate (ethyl dec-9-enoate)	888

a For each segment only compounds showing significantly different peak area values between treatments are listed. b Experimentally determined linear retention indices. c

The data generated by hyphenated chromatographic techniques such as GC-MS or LC-MS are especially information rich. Feature extraction such as peak picking or peak integration in single ion chromatograms, total ion chromatograms or deconvoluted signals are the most common approaches to extract information from chromatographic data. The results are in relatively small data tables which are straightforward to analyse[START_REF] Behrends | A software complement to AMDIS for processing GC-MS metabolomic data[END_REF][START_REF] Stein | An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data[END_REF][START_REF] Aggio | Metab: an R package for highthroughput analysis of metabolomics data generated by GC-MS[END_REF][START_REF] Want | Processing and Analysis of GC/LC-MS-Based Metabolomics Data[END_REF][START_REF] Luedemann | TagFinder for the quantitative analysis of gas chromatography -mass spectrometry (GC-MS)-based metabolite profiling experiments[END_REF][START_REF] Smith | XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification[END_REF][START_REF] Vestner | Investigation of the volatile composition of pinotage wines fermented with different malolactic starter cultures using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOF-MS)[END_REF]. Although various peak integration algorithms and software packages have been developed[START_REF] Dixon | An automated method for peak detection and matching in large gas chromatographymass spectrometry data sets[END_REF][START_REF] Furbo | Automated peak extraction and quantification in chromatography with multichannel detectors[END_REF][START_REF] Hastings | New algorithms for processing and peak detection in liquid chromatography/mass spectrometry data[END_REF][START_REF] Vivó-Truyols | Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals: Part I: Peak detection[END_REF], automated peak integration remains troublesome due to coelution and potential erroneous peak integration and/or assignment. Time consuming manual correction of the results is often necessary. Moreover, relevant information from the raw data can be lost due to such feature extraction before multivariate data analysis[START_REF] Skov | A new approach for modelling sensor based data[END_REF][START_REF] Ballabio | Classification of GC-MS measurements of wines by combining data dimension reduction and variable selection techniques[END_REF]. Deconvoluting chromatographic signals can also be time-consuming in terms of model construction and evaluation of results

If the means of the columns of X are zero, the summed cross-products for two variables will be proportional to their covariances (covariance matrix). If, in addition, the variances of the columns of X are unity, then the cross-products for two variables will be equal to the correlation coefficient for those two variables (correlation matrix).

The concatenation of two arbitrary row vectors x = (x 1 , . . . , x n ), y = (y 1 , . . . , y n ) is defined as:x y = (x 1 , . . . , x n , y 1 , . . . , y n ).

The concatenation of two arbitrary row vectors x = (x 1 , . . . , x n ), y = (y 1 , . . . , y n ) is defined as:x y = (x 1 , . . . , x n , y 1 , . . . , y n ).

ACKNOWLEDGEMENTS

I would like to express my sincere gratitude to the following people and organizations for their contribution throughout this study: My supervisors, Prof. Dr. Doris Rauhut and Prof. Dr. Gilles de Revel, for their supervision and for providing me the rare opportunity of a co-supervised thesis between two European universities. I would like to thank them particularly for all the effort and time they have invested into the realization of this cotutelle.

CHAPTER III

Development of new approaches for non-targeted GC-MS data analysis 3.1 Introduction differences between samples. PC1 explaining 52.8 % of variance (Figure 3.29) shows the difference between co-inoculated and sequentially inoculated wines. The segments [START_REF]BIBLIOGRAPHY[END_REF]24,28,31,35,38,45,50,53 and 67 are mainly positively correlated with the coinoculated wines. Similar information on the difference between co-inoculated and sequentially inoculated wines is revealed from PARAFAC component 1 of approach 1 (Figure 3.20). The PARAFAC model reveals however more segments contributing to this differentiation. The wines fermented with yeast Uvaferm RBS and the wine sequentially fermented with the yeast/bacteria combination of Lalvin Clos and Enoferm Beta (clos beta seq) are separated from all other wines on PC2 (14.4 % explained variance; Figure 3.29). The three sequentially inocluated wines with the yeast/bacteria combinations Uvaferm RBS/O-Mega (rbs 271), Uvaferm RBS/Lalvin VP41 (rbs 41) and Lalvin Clos/Enoferm Beta (clos beta) correlate with the segments 68, 69, 64, while the two wines co-inoculated with the yeast/bacteria combination Uvaferm RBS/O-Mega (rbs 271), Uvaferm RBS/Lalvin VP41 (rbs 41) correlate with the segments 24 and 8. The PARAFAC model of approach 1 reflects similar information on the difference of the sequentially inoculated yeast/bacteria combination Lalvin Clos/Enoferm Beta (clos beta) on component 2 (Figure 3.20) and on the differences of the wines fermented with the yeast Uvaferm RBS on component 5 (Figure 3.22).

The information from the PARAFAC model of approach 1, however, shows this information on two separate components. Moreover, approach 1 gives more information of the importance of other segments for the observed groupings of samples, such as for segment 1, which contributes to the difference of the wines fermented with the yeast starter culture Uvaferm RBS.

PC3 and PC4 explaining 10.5 % and 7.1 % of variance, respectively, are displayed in Figure 3.30. The most interesting information on PC3 and PC4 are the correlation of the segments 35 and 43 with the wine co-inoculated with the Lalvin Clos and Lalvin PN4 (clos PN4) and the correlation of the segments 68 and 69 with the wine USA) and the freely available N-way toolbox [START_REF] Andersson | The N-way Toolbox for MATLAB[END_REF].

Deconvolution of important chromatogram segments and identification of compounds using AMDIS

Peaks in chromatogram segments which had congruence loadings higher than 0.3 ('weak to strong correlation') were deconvoluted using the software AMDIS [START_REF] Stein | An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data[END_REF] for the same reason as described in Section 4.2.3. Deconvoluted mass spectra were compared with the NIST08 library [START_REF] Stein | NIST Mass Spectral Search Program[END_REF]. Linear retention indices (LRI) were calculated using a homologous series of n-alkanes and compared with literature values to confirm tentative identifications. Deconvoluted peak areas were obtained using the batch processing function of AMDIS and exported as .txt files for further data analysis.

Partial projective mapping with free choice profiling and multiple factor analysis (MFA)

Partial projective mapping with free choice profiling was performed in the same week as the GC-MS analyses with 18 wine experts from different research departments of the Hochschule Geisenheim University, Germany. Thirteen wines were examined; the twelve treatments plus an additional sample of the wine from turbid must of young grapevines fermented with Zymaflore X5 (X5rep Y T), which was used as a control sample to monitor the quality of obtained sample groupings. Fifty mL of every wine were presented in DIN Sensus wine tasting glasses (Zwiesel Kristallglas AG, Zwiesel, Germany). The glasses were labelled with random three-digit codes and covered with plastic Petri dishes for orthonasal evaluation. All wines were presented simultaneously in random order to the assessors. The tasting was conducted at room temperature in an ISO 8589:2007 certified tasting room equipped with a cubicle for each taster. The assessors were encouraged to position wines that they perceived as Zymaflore X5 and Oenoferm Klosterneuburg. These results were expected as EC 1118 is S. bayanus, which are known for a robust fermentation performance. The effects observed for the age of grapevines and the degree of must clarification on the fermentation are probably caused by different nutrition scenarios in the must. Moreover, solids in turbid musts provide a larger inner surface where yeast cells can adhere on which favours yeast activity/growth.

Non-targeted HS-SPME-GC-MS analysis

GC-MS analysis was performed using HS-SPME sample preparation due to the simplicity of this technique for wine analysis regarding full automation, speed and sensitivity. For HS-SPME a 100 µm PDMS fibre was used, as PDMS degradation products are easy to identify by means of siloxane fragments in their mass spectra.

To facilitate a higher sample throughput a fast temperature ramp was applied to keep the GC runtime low. Lower chromatographic resolution was acceptable, as the non-targeted data analysis method used here takes the full mass dimension into consideration. The stability and reproducibility of the GC-MS system was monitored throughout the analysis period, which is particularly important when non-targeted analysis is applied. A fibre-blank injection and a hydro-alcoholic standard solution containing common wine volatiles were injected regularly after every eight sample analyses. This monitoring ensured the reproducibility of analyses. Absolute peak area values of the internal standard did not significantly (ANOVA, α = 0.05, ) differ among treatments, even though some wines had higher residual sugar contents. This fact made the use of an internal standard -although one was added to each sample as a precautionary measure -unnecessary. is therefore very unlikely. A possible negative sensory impact of this compound on wine aroma has however been described before. Marais and Pool found a negative correlation between the intensity of the young wine bouquet and levels of hexanol, 2-methylpropanol and 3-methylpropanol in Riesling and other varieties [START_REF] Marais | Effect of storage time and temperature on the volatile composition and quality of dry white table wines[END_REF]. Moreover, Rankine and Pococx reported in their sensory study that higher concentrations of hexanol tended to give wine a foreign aroma, which they regarded as a reduction in quality [START_REF] Rankine | Influence of yeast starin, grape variety and other factors; and taste thresholds[END_REF] . Glucose, fructose, fermentable sugar, and density also correlate with these wines as can be seen in the representation of the additional chemical data in Figure 5.10(d). The higher sugar levels of these wines result from stuck fermentation close to the end (Figure 5.1). Interestingly, although all samples were only orthonasally evaluated, the wines with residual sugar were grouped together. The acetate esters matograms combined with PARAFAC modelling revealed differences between wine samples. Not all differences between samples discovered by the PARAFAC approach could be fully reproduced by simple PCA on autoscaled peak table data (deconvoluted peak areas), which points out the benefit of the PARAFAC approach over only PCA on peak tables. The use of full factorial experimental design with visual examination of the results of the fingerprinting approach and MLR revealed main effects and interaction effects between studied factors on groups of compounds. The integration of information from fast GC-MS screening of volatiles and rapid sensory profiling by means of the multi-block PCA method MFA facilitates the correlation of compounds with sensory descriptor groups and wine samples. These correlations have to be very carefully interpreted as a correlation does not necessarily imply causality.

Main and interaction effects of the factors vine age, must turbidity and yeast strain on the aroma of Riesling wines could be shown. For instance, the sensory impression of wines made from turbid musts of old and young grapevines were rated differently for two of the three yeast starter cultures. Different yeast starter cultures reacted differently to must turbidity, and this effect even depended on the composition of the must (must from old grapevines vs. must from young vines). The results presented herein emphasise the need for multifactorial approaches including multivariate statistics to study the impact of oenological and viticultural factors on wine aroma. The discovered effects are very likely to be influenced by even more factors such as grape variety, vintage, clones, location of the site, and others. Full factorial designs however quickly become too big and complex the more factors and factor levels are included.

The application of screening designs prior to full factorial examination of influencing factors could be a solution to this problem in future studies.

CHAPTER VI

General conclusions

The primary goal of this study was the development and application of a new data analysis approach for non-targeted gas chromatography mass spectrometry (GC-MS) fingerprinting data of wine volatiles with a special focus on the avoidance of retention time correction between samples and feature selection. Matrix algebra and chemometrics were used for mathematical transformations and modelling of twodimensional GC-MS chromatograms of multiple samples. Moreover, merging of data from non-targeted GC-MS fingerprinting of volatiles and fast sensory profiling was another focus of this study.

In the first chapter, general background on targeted and non-targeted chromatographic analysis with an emphasis on chemometrical modelling of chromatographic data is provided. Furthermore, the composition of wine aroma in terms of the volatile composition of wine and the use of gas chromatography in wine analysis is reviewed.

A short introduction into rapid sensory profiling of wine is also given.

The development of two new chemometric approaches for non-targeted GC-MS data is presented in Chapter three. A major drawback of conventional data analysis approaches is the necessity of retention time alignment of peaks between samples. Some existing chemometric approaches use multivariate (or multi-way) models which take peak shifts between samples into account to deconvolute predefined chro-

APPENDICES

Schematic representation of approach 1

The following Figure A.1 shows a schematic representation of approach 1 which is described in Section 3.4. MATLAB codes for approach 1 and approach 2 are provided in the Appendices C and D, respectively.

APPENDIX B

Approach 1 with class centroid centering and scaling to intra-class variance applied to the compilation matrices Y k (Equation 3.9 in Section

3.6)

In the following the results of the application of approach 1 to the HS-SPME-GC-MS analysis of the Cabernet Sauvignon wines (Section 3.6), where class centroid centering and scaling to intra-class variance (12 classes, one for each treatment) was applied to the compilation matrices Y k (Equation 3.9) are shown. A 9 component PARAFAC model explaining 98.1 % of variation in the dataset was obtained. Note that both the final three way array Z were centered across sample modes (mode one and two) and scaled to unit variance within the segment mode (mode 3). In general very similar information on the grouping of samples is obtained compared with the PARAFAC model on the unscaled compilation matrices and the results of