B Bonnefille

C Guillemain

Cecile Miege

Christelle Margoum

Non-target analysis for the evaluation of surface water contamination: influence of sample preparation prior to UHPLC-HRMS analysis

Conclusion & Perspectives

This study highlights that the sample preparation influences the fingerprints obtained by non-target analyses of surface water samples. Each sample preparation tested allows the detection of specific and common features. Evaluation of the informativeness, the specificity, and especially the repeatability of sample preparation methods is a pre-requisite to evaluate surface water contamination without any a priori. Among the different sample preparation methods tested, the method appears as the most suitable to study surface water contamination.

Analysis in negative electrospray ionization mode has to be realized to confirm the SPE method selection. Moreover, structural elucidation of features with a fold change > 50 specific to each sample preparation method may reveal the potential of a method to extract compounds belonging to the same chemical family. Following the same workflow, structural elucidation of the more abundant features in the SPE extracts lead to the identification of:

The method allow the detection of more features in the water extracts than other methods, with about 100 additional signals.

Each SPE method allows the detection of several specific features, especially the method .

 The best compromise between repeatability and informativeness of data obtained is the method 4 4 4

For this study, methods with more than 60% SPE features presenting a CV lower than 30% are considered as repeatable.

12 features presenting an area fold change > 50 were common to all methods. Among them, the feature M380T481 (m/z 380.2568; retention time 481 sec.) with a fold change > 500 was taken as example for structural elucidation.

Results

Repeatability of the SPE methods

Data processing (XCMS and CAMERA) Material & Methods

A surface water sample collected in a French river (Gier, Rhône, France) was extracted following five different solid phase extraction (SPE) methods. Analytical blanks, SPE blanks, and quality controls (QC) were also prepared. Fingerprints were generated by ultra-high performance liquid chromatography coupled to high resolution mass spectrometry (UHPLC-HRMS/MS).

The data were processed and the SPE methods repeatability were characterized on the basis of all the features detected in the fingerprints.

To evaluate the SPE methods informativeness and specificity, the features satisfying the following criteria have been retained:

• Signal-to-noise ratio (S/N) compared to analytical blank > 3

Introduction & Objectives

Identifying organic contaminants present at trace level concentration in aquatic environments is of major concern for water quality monitoring. Suspect and nontarget analyses are increasingly applied to study the presence of emerging contaminants and their transformation products in waters. The sample preparation is the first crucial step to be optimized before qualitative evaluation of water contamination by organic compounds.

The main objectives of our study were to evaluate the quality of the data obtained with different sample preparations (extraction repeatability, informativeness, and specificity), and to implement a strategy for molecular structure elucidation.

Non-target analysis for the evaluation of surface water contamination: influence of sample preparation prior to UHPLC-HRMS analysis

  the French Agency for Biodiversity (AFB) for financial support. This work was realized within the framework of AQUAREF program. Acknowledgement [1] Schymanski et al. Non-target screening with high-resolution mass spectrometry: critical review using a collaborative trial on water analysis. Anal. Bioanal. Chem. 407, 6237-6255 (2015) [2] Sumner et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211-221 (2007)

  Common and specific features detected in water samples, depending on the SPE method.Performed on sorted data.Percentage of SPE featuresPercentage of SPE features presenting a CV < 30% for each SPE method

••

  features with a fold change > 50, depending on the SPE method. Performed on sorted data. • Molecular formula determination (MassLynx 4.1 + ChemSpider)  2 relevant chemical formulas: C 20 H 34 N 3 O 4 / C 25 H 34 NO 2 Comparison of fragmentation data with European MassBank Database  One proposition with a score > 70%: Celiprolol (pharmaceutical) • Data obtained: MS, MS isotopes, MS 2 and MS 2 library spectrum match  Level 2a identification (Probable structure, [1]) The signal is present in the SPE extracts ( ) and missing in the analytical blanks ( ) and in the SPE blanks ( ) • Isotopic massif of M380T481 (M, M+1 and M+2) is detected, revealing the absence of Cl or Br in the structure • The detected signal may be an organic contaminant Extracted Ion Chromatogram of M380T481

  Irstea, UR RiverLy, Centre de Lyon-Villeurbanne, 5 rue de la Doua, CS 20244, 69625 Villeurbanne, France benilde.bonnefille@irstea.fr


Anne Guillemain

Claire Togola

F Soulier

D Lestremeau

Cecile Cabillic

C Miege

Margoum

How solid phase extraction conditions affect suspect and non-target screening for the evaluation of surface water contamination

B. Bonnefille, C.

Identifying organic contaminants present at trace level concentration in aquatic environments is of major concern for water quality monitoring. Suspect and non-target analyses are increasingly applied to study the presence of emerging contaminants and their transformation products in waters. The sample preparation is the first crucial step to be optimized before using suspect and non-target-screening (NTS) approaches for qualitative evaluation of water contamination by organic compounds.

As part of the action of AQUAREF (French reference laboratory for environmental monitoring in the Water Framework Directive), the main objective of our study was to assess the influence of sample preparation on suspect and non-target screening results.

Introduction & Objectives Conclusion & Perspectives

This study highlights that the sample preparation influences the chemical fingerprints obtained by non-target analyses of surface water samples. Each sample preparation tested allows the detection of specific and common signals. Nevertheless, both the suspect and the non-target approaches reveal the method 4 is the most suitable regarding the informativity, the specificity, and especially the repeatability of the sample preparation methods tested.

The assessment of the sample preparation is a pre-requisite to evaluate surface water contamination without any a priori.

To go further, analyses in negative ionization mode have to be realized to confirm the SPE method selection. Moreover, a structural elucidation process of common and specific signals may highlight other SPE method specificities. The data were processed to perform suspect screening and NTS.

Suspect screening

Results

1/ Repeatability of the whole analytical sequence

• The PCA score plot reveal that QC injections are clustered together (in red).

• The CV calculation of the QC signals revealed that 82% of the signals present a CV <30%.

 The analytical repeatability was good.

The data processing can therefore be further investigated.

PCA score plot of the full-scan data obtained after SPE and before sorting data according to their CV QC • Extraction methods with more than 70% SPE signals presenting a CV lower than 30% are considered as repeatable. Performed on sorted data.

Non-target screening

 The best compromise between data repeatability and informativity is obtained with the method .

• The method allowed the detection of more signals in water extracts than other methods, with about 100 additional signals.

• Each SPE method allowed several specific signals detection, particularly the method .

• The method allowed the extraction of compounds with higher m/z in contrast to the method .

• The method allowed the extraction of more polar compounds, considering retention time distribution. • Among the 28 suspect compounds, 4 were not detected in any extract (cotinine, methylparaben, metolachlor-ESA, and paracetamol).

• Based on the detector counts of the suspect compounds, the methods 1 & 3 provide a lower extraction efficiency than the other methods. On the opposite, the methods 2 & 4 allowed a higher extraction efficiency for most of the 28 suspect compounds than the other methods.

• Most of the pesticides detected in the sample extracts are currently banned (e.g. diuron, terbutryn).

• Concerning the pharmaceuticals, drugs acting on the nervous system were the most commonly detected (e.g. oxazepam, venlafaxine), as well as betablockers (e.g. atenolol, metoprolol). The authors thank the French Agency for Biodiversity (AFB) for financial support. This work was realized within the framework of AQUAREF program.

A

  surface water sample collected in a French river (Gier, Rhône, France) was extracted following five different solid phase extraction (SPE) methods. Analytical blanks, SPE blanks, and quality controls (QC) were also prepared. Chemical fingerprints were generated by ultrahigh performance liquid chromatography coupled to high resolution mass spectrometry in positive ionization mode (UHPLC-HRMS/MS).

  Suspect screening was performed by comparison of the data with a list of 28 compounds, including compounds such as pesticides, pharmaceuticals, sweeteners. Suspect compounds screening was performed by comparison of their measured and theoretical monoisotopic mass [M+H] + and their fragments (Δ = ± 5 ppm). To evaluate by non-target analysis the SPE methods repeatability, informativity, and specificity, the signals satisfying the following criteria were retained: • Quality control (QC) signals coefficient of variation (CV) <30% (analytical repeatability assessment is good if >70% of the signals in all QC injections have a CV <30% [3]) (n=11) • SPE signals CV <30% (extraction repeatability, n=3 per method) • Signal-to-noise ratio (S/N) compared to analytical blank > 3 • S/N compared to SPE blanks > 3 UHPLC-HRMS/MS C18 (HSS T3, 2.1x100 mm; 1.8 µm, Waters) ESI+ (G2-S QTof, Waters) MassLynx 4.1 (Waters) How solid phase extraction conditions affect suspect and non-target screening for the evaluation of surface water contamination 1 Irstea, Lyon Villeurbanne, France 2 BRGM, F-45060 Orléans, France 3 INERIS, Verneuil-en-Halatte, France 4 LNE, Paris, France Bénilde Bonnefille 1 , Céline Guillemain 1 , Anne Togola 2 , Coralie Soulier 2 , François Lestremeau 3 , Daniel Léonço 3 , Sophie Lardy-Fontan 4 , Julie Cabillic 4 , Cécile Miège 1 , Christelle Margoum 1 Contact: benilde.bonnefille@irstea.fr

  next steps of this study.

5

  Number of common and specific signals detected in water extracts, depending on the SPE method.

  Distribution of m/z (left) and retention time (right) of signals detected.Performed on sorted data.Examples of suspect compounds better extracted by one of the SPE methods tested

Acknowledgement


est destinée au dépôt

Large lakes of South-Western France are largely concerned by recreational activities and constitute an important touristic place during summer. These lakes have bathing areas regularly monitored for public health. In France, regulation impose biological monitoring, especially for cyanobacteria that could be potentially toxic. However, current survey induces long time delay detrimental to health security and resulting in high financial costs.

© Irstea

This new decision tree provides at least the same safety level and is, sometimes, more precautionary than the regulatory method. This new method need however to be tested on other lakes with different cyanobacteria taxa and biomass, but provide encouraging results for bathing areas management and is already used for management of lake beaches of the large lakes of South-Western France. Sylvia Moreira 1 , Christophe Laplace-Treyture 1 , Sarah Gogin 1 , Laurent Pickhahn 2 , Mélissa Eon 1 , Aurélien Jamoneau 1

Conclusions and perspectives

1 Irstea, UR EABX, 50 avenue de Verdun, F-33612 Cestas Cedex, France.

  An operational monitoring system for cyanobacterial blooms: application to water bodies in the South-Western France Sylvia Moreira, Christophe Laplace-Treyture, S. Gogin, L. Pichahn, Mélissa Eon, Aurélien Jamoneau To cite this version: Sylvia Moreira, Christophe Laplace-Treyture, S. Gogin, L. Pichahn, Mélissa Eon, et al.. An operational monitoring system for cyanobacterial blooms: application to water bodies in the South-Western France. Big lakes -Small World ELLS-IAGLR-2018, Sep 2018, Evian, France. pp.1

2

  Communauté de Communes des Grands Lacs (CCGL), 136 rue Jules Ferry B.P. 64, F-40161 Parentis-en-Born Cedex, France.


2

Section II a detailed description of our method. We then analyze the scalability of our approach with state of the art deep nets and Spot-7 and Sentinel-2 images (Section III). We finally discuss the main advantages and limits of our approach in Section IV.

II. METHOD

A. Overview

The Orfeo ToolBox (OTB) is a library for RS image processing, built on top of an application development framework widely used in medical image processing, the Insight Toolkit (ITK) [START_REF] Yoo | Engineering and algorithm design for an image processing API: A technical report on ITK -The Insight Toolkit[END_REF]. The machine learning framework of OTB is able to process large datasets at continental scale for land mapping [START_REF] Inglada | Operational high resolution land cover map production at the country scale using satellite image time series[END_REF] and benefits from High Performance Computing (HPC) architectures like clusters [START_REF] Cresson | A generic framework for the development of geospatial processing pipelines on clusters[END_REF]. TensorFlow (TF) is a library for dataflow programming. It is a symbolic math library, and is intensively used for machine learning applications such as deep nets. It can also operate at large scale in HPC architectures like GPU. We aim to extend the existing rich machine learning framework of OTB with TF. Implementation of state of the art deep nets should be enabled with the minimum effort, and the opportunity to apply them on RS images must be granted to non-developers, namely users. The existing user-oriented machine learning framework of OTB must be preserved. We also sought the implementation of a component which can be used in a transparent way inside any OTB pipeline, to enable the combination of already implemented approaches with DL. In the following sections, we provide description of the libraries processing frameworks (Sections II B and II C). Then we introduce our low-level OTB compliant component in Section II D and our new user-oriented OTB applications in Section II E. Finally, we analyze performances of our approach with state of the art RS deep nets in Section III.

B. Libraries paradigms

This section describes the processing logic of OTB and TF.

OTB workflow

A pipeline refers to a directed graph of process objects, that can be either sources (initiating the pipeline), filters (processing the data) or mappers (typically, write a result on disk). Sources and filters can generate one or multiple data objects (e.g. image, vector). In the same way, filters and mappers consume one or multiple data objects. The architecture of OTB inherits from ITK and hides the complexity of internal mechanisms for pipeline execution, which involve several steps. Detailed description of the pipeline mechanism can be found in [START_REF] Ibez | ITK[END_REF] including figures and sequence diagram. The execution of a pipeline starts from a mapper, triggering its upstream filter(s). When a filter is triggered, information about mandatory input data (i.e. information about output data of upstream process object(s)) is also requested upstream. In this way, it is propagated back through the pipeline, from mappers to sources via filters. Once this request reach sources, data objects information are generated (e.g. image size, pixel spacing) then propagated downstream to mappers. It should be noted that filters can potentially modify these information, according to the process they implement (e.g. changing image size) which is the case with image resampling for instance. Finally, they reach the mapper, initiating the data processing. Information regarding the size of the image that will be produced, is then used by the mapper to choose a splitting strategy. The default splitting scheme is based on the system memory specification. Other strategies can be chosen, e.g. striped or tiled regions with fixed dimensions. Then, the mapper requests its input image to upstream filter(s) sequentially, region after region. The data request and generation is handled through the pipeline in the same way as for the information: once the request reaches the sources, initiating the pipeline, the requested region is produced, then processed through filters, to finally end in the mapper. The pipeline execution continues with the next image region until the entire output is generated. This mechanism, named streaming, enables the processing of very large images regardless the memory consumption of the process objects composing the pipeline.

TF workflow

TF uses symbolic programming, which distinguish definition of computations from their execution. In TF, tensors are abstraction objects of the operations and objects in the memory, simplifying manipulation regardless the computing environment (e.g. CPU or GPU). A TF model consists in operations arranged into a graph of nodes. Each node can viewed as an operation taking zero or more tensors as inputs, and producing a tensor. This data flow graph defines the operations (e.g. linear algebra operators), but the computations are performed within the so-called session. A high-level API enables the construction of TF graphs, and the session runs the graph in delegating calculations to low level, highly-optimized routines. Among tensors, we can distinguish concepts such as Placeholders, Constants and Variables. A Placeholder is a symbol hosting input data, e.g. a set of images. As its name indicates, Constants are tensors with constant values, and Variables hold non persistent values, e.g. parameters to estimate during training. Variables must be explicitly initialized, and can be saved or restored during a session along with the graph. A number of tools enable design of TF models, like the TF Python API or user-oriented software developed 3 by the TF community.

C. Flowing the pipeline

In this section, we present prerequisites for the integration of a process object that runs TF session for generic deep nets, in a OTB pipeline with RS images as data objects. In the pipeline workflow, the generation of images information and the requested regions computation by process objects, are crucial steps. We denote the spacing the physical size of a single RS image pixel. The generation of output images information includes origin, spacing, size, and additional RS metadata e.g. projection reference. Process objects must also propagate the requested regions to input images. Regarding deep nets implementation, and particularly CNNs, this process must be carefully handled. CNNs usually involve several operations, mostly including a succession of convolutions, pooling, and non-linear functions. There is also a number of deriving operators e.g. transposed convolution. Most of these operators modify the size and spacing of the result. For example, convolution can change the output image size, depending its kernel size and input padding. It can also change the spacing of the output if performed with strides, that is, the step of which is shifted the filter at each computation. Pooling operators are also a common kind of operator that modify the output size and spacing, depending of the stride and the size of the sliding window. We should note that a number of other operators change size and scale the spacing of the output.

Considering a process object implementing such operations, it must propagate requested regions of images to its inputs. In the following, we introduce a generic description of size and spacing modifications that a deep net induces in processing RS images. We name the scaling factor of a particular output of the net, the ratio between the output spacing and a reference spacing (typically one input image feeding the deep net). This parameter enables the description of any change of physical size of the pixel introduced by operators such as pooling or convolution involving non-unitary strides. In addition, each input has its own receptive field , the input space that a particular output of the net is affected by. In the same way, each output has its own expression field, the output space resulting from the receptive field. Images regions and spacing modifications induced by a TF graph are thus defined with the receptive field of inputs, and the scaling factor and expression field of outputs.

D. Running TF session in process object

We introduce a new OTB process object which internally invokes the TF engine and enforces the pipeline execution described in Section II C and hence, can seamlessly process large images. It takes one or multiple input images, and produces zero or multiple output images. The input placeholders of the TF model are fed with the input images of the process object. After the session run, computed tensors are assigned to the process object outputs. Placeholders corresponding to input images, as well as tensors corresponding to output images, are both specified as named in the TF model. The process object uses receptive field of inputs, as well as expression field and scaling factor of outputs, to propagate requested input images regions and generate information of output images. Finally, two processing modes are currently supported:

1. Patch-based
Extract and process patches independently at regular intervals. Patches sizes are equal to the receptive field sizes of inputs. For each input, a tensor with a number of elements equal to the number of patches feds the TF model.

Fully-convolutional

Unlike patch-based mode, it allows the processing of an entire requested region. For each input, a tensor composed of one single element, corresponding to the input requested region, is fed to the TF model. This mode requires that receptive fields, expression fields and scale factors are consistent with operators implemented in the TF model, input images physical spacing and alignment. Blocking artifact is avoided by computing input images regions aligned to the expression field sizes of the model, and keeping only the subset of the output corresponding to the requested output region.

E. New OTB applications

OTB applications generally implement pipelines composed of several process objects connected together. We first provide a new application dedicated to RS images sampling suited for DL. Then, using our new filter described in Section II D, we provide new applications for TF model training and serving. Finally, we introduce some new applications consisting in an assembly of multiple OTB applications. All these new applications are integrated seamlessly in the existing machine learning framework of OTB.

Sampling

The existing machine learning framework of OTB includes sample selection and extraction applications suited for geospatial data like vector layers and RS images. Multiple sampling strategies can be chosen, Author-produced version of the article published in IEEE Geoscience and Remote Sensing Letters, 2019, 16 (1), pp. 25-29 The original publication is available at https://ieeexplore.ieee.org Doi: 10.1109/LGRS.2018.2867949 with fine control of parameters. However, DL on images usually involves reference data made of patches, but the existing application performs pixel-wise sample extraction. We introduce a new application that performs patches extraction in input images, from samples positions resulting from the existing sample selection application of OTB. As we want to stick to RS images files formats, samples are concatenated in rows to form one unique big image of patches. Typically, considering a number n of sampled patches of size [S row , S col , S ch ], the resulting image have a size of [n ⋅ S row , S col , S ch ]. The main advantage of this technique is that pixel interleave is unchanged (typically row, column, channel), guaranteeing the efficient copy and processing.

Training

Since the high level TF API is Python, we provide a light Python environment to allow developers to build and train their models from patches images produced using our sampling application described in Section II E 1. As there is already a number of available TF models and existing user-oriented opens-source applications to create TF models, we chose not to focus it.

Our contribution for RS images deep net training is a user-oriented OTB application dedicated for training existing TF models. The application can import any existing TF model. It can restore from file model variables before the training, or save them to disk after training. Thus this application can be used either to train a particular model from scratch, or perform fine tuning depending on the variables restored. The training can be performed over one or multiple inputs given their corresponding TF placeholders names and provide usual evaluation metrics.

Model serving

We introduce a single OTB application dedicated to TF model serving, that implements a pipeline with the filter described in Section II D. It produces one output image resulting from TF model computations. As the entire pipeline implements the streaming mechanism described in Section II C, it can process one or multiple images of arbitrary sizes. The user can adjust the produced images blocks sizes, thanks to the internal OTB application architecture. This application provides a generic support for the operational deployment of trained deep nets on RS images.

Composite applications

Composite applications are OTB applications connected together. Recent studies have suggested that deep nets features can be used as input features of In the sake of demonstrating the operational aspect of our approach, we implement a composite application that reuse our model serving application (described in II E 3) as input of two existing OTB applications: the TrainIm-agesClassifier and ImagesClassifier applications respectively dedicated to train a classifier and perform an image classification.

III. EXPERIMENTS

In this section, we analyze performances of our TF model serving application. We conduct a series of experiments on two representative state of the art deep nets for RS images, the Maggiori et al. fully convolutional model [START_REF] Maggiori | High-resolution aerial image labeling with convolutional neural networks[END_REF] and the Ienco M3 data fusion model [START_REF] Ienco | Land cover classification via multitemporal spatial data by deep recurrent neural networks[END_REF]. Both perform land cover classification, but are different in terms of architecture as well as implementation. The model of Maggiori is a single-input CNN composed exclusively of convolutions, pooling and activation functions, allowing to process one image region with a single convolution. The M3 model is an hybrid CNN-RNN model that inputs time series (TS) and one VHRS image. Unlike the Maggiori model, it consumes patches of the VHRS image and one pixel stack of time series, to perform a single estimation at one only location. Table 1 RAM). The second is a server with GPU support (Intel (R) Xeon (R) CPU E5-2667 v4@3.20Ghz with 256 GB of RAM and TITAN X GPU). Libraries versions are OTB 6.7 and TF 1.7. We measure run times of our applications using the two models on both configurations, for different computed images sizes and various tile size. We presents separately the results for both models because their processing time differs largely due to the fact that M3 is more complex than the Maggiori model. Figure 1 shows that processing time is linear to the number of pixels of the produced image, regardless parameters. Very large input datasets are entirely processed thank to the streaming mechanism, even on the basic workstation with only 16Gb RAM. TF computations are benefiting strongly from the GPU support especially for image convolution, but CPU workstation processing time is reasonable with less than 24 hours for M3 (Fig. 1b) and less than 1 hour for the Maggiori model (Fig. 1a).

IV. DISCUSSION

A. Operational deep learning framework for RS images

Our first goal was to provide a generic DL framework for RS images processing. We propose a solution which takes roots in the OTB library. The original machine learning framework was enriched to embed TF for training and using deep neural networks. We introduce a new component enforcing the OTB workflow, that developers can implement in pipelines. We present a set of new applications that users without programming knowledge can operate: patches sampling, model training and serving, and also training and classification applications performing on features of any deep net exported as a TF model. Our approach is successfully applied to common RS images with two representative state of the art deep nets. Processing times measurements have shown a great scalability and also that our applications can run deep nets without restriction on dataset size and regardless hardware configuration.

B. Limits and further research

It should be noted that the presented method also has limits. The user has responsibility in providing crucial parameters to applications. In particular, receptive field, expression field and scale factor of deep nets. In future development, those parameters could be extracted from the serialized TF graph. In the same way, the memory footprint is computed within the OTB pipeline, but does not takes in account the memory consumed internally by TF during the execution of the computational graph: the user is thus responsible of fine tuning using the OTB application engine parameters.

V. CONCLUSION

This work was carried out with a view toward processing remote sensing (RS) images using deep learning (DL) techniques from the user perspective. We propose a generic framework based on Orfeo Toolbox (OTB) and TensorFlow (TF) libraries. We have successfully applied existing state of the art deep nets on common RS images using our framework and shown a good computational efficiency, without restriction on images sizes and regardless hardware configuration. Our approach enables users without coding skills to use deep nets in their RS applications, and developers to create operational RS processing pipelines benefiting from the development framework of OTB library. Our approach allows the combination of the existing OTB machine learning framework with deep nets. The integration of DL processes in high performance computing architectures is enabled thank to the heterogeneous devices supported by the used libraries. Further research could focus on improving the automatic retrieval of nets parameters and memory footprint. The source code and documentation corresponding to the implementation presented in this paper is available at [START_REF]OTBTensorflow remote module for orfeo toolbox[END_REF], and the exposed framework will be proposed as official contribution in the forthcoming releases of OTB.

Figure 1 .

 1 Figure 1. Measured processing times for different images sizes for (a) the Maggiori model (MAG17) and (b) Ienco M3 model (M3 )

Table 1 .

 1 Models parameters

		Maggiori FC17	Ienco M3
	RF	80x80 (VHRS ) 1x1 (TS ), 25x25 (VHRS )
	EF	16x16	1x1
	Mode	Fully-conv	Patch-based
	Reference	VHRS	TS

Table 2 .

 2 Characteristics of the datasetIdProduct type Size (col × row × band) Pixel spacing

	VHRS Spot-7 PXS	24110 × 33740 × 4	1.5 meters
	TS	S2 Time series 3616 × 5060 × 592	10 meters
	algorithms like classification, leading state of the art
	results. Regarding RS image classification, OTB al-
	ready implement a number of algorithms in its classifi-
	cation application, e.g. SVM, Random Forests, boost
	classifier, decision tree classifier, gradient boosted tree
	classifier, normal Bayes classifier.

Author-produced version of the article published in IEEE Geoscience and Remote Sensing Letters, 2019, 16 (1), pp. 25-29 The original publication is available at https://ieeexplore.ieee.org Doi: 10.1109/LGRS.2018.2867949

ACKNOWLEDGMENTS

The author thanks the OTB team, Dino Ienco and Emmanuel Maggiori. This work was supported by public funds received through GEOSUD, a project (ANR-10-EQPX-20) of the Investissements d'Avenir program managed by the French National Research Agency. The author would also like to thank the reviewers for their valuable suggestions.

Author-produced version of the article published in IEEE Geoscience and Remote Sensing Letters, 2019, 16 (1), pp. 25-29 The original publication is available at https://ieeexplore.ieee.org Doi: 10.1109/LGRS.2018.2867949


F Herard

S Sauvagère

A El Anjoumi

El Amrani

D Guillebault

Christophe Laplace-Treyture

Sylvia Moreira

A Ortega Pizarro

J M Rodriguez

C L Manes

Sensitive biosensing of potentially toxic cyanobacteria for risk application in freshwater environments

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Harmful cyanobacterial blooms are a major threat to freshwater ecosystems globally weighing down the Good Environmental Status of waters and posing risk for human health. The system established by current French regulation to detect and monitor these blooms is based on microscopic observations with a 24 to 48 hours delay, while requiring trained taxonomists. To improve and reduce analysis times, biosensors are a highly promising technology that can produce near real-time measurements that could be used by early warning systems safeguarding human and freshwater ecosystem health. To this end, we develop a rapid, sensitive and economical test to identify and daily monitor the blooms of potentially toxic cyanobacteria in aquatic environments based on their genetic identity. Nevertheless, to validate this innovative solution, we still have to compare our data with microscopic traditional cell counting and biovolume estimation present in the samples.

Sensitive biosensing of potentially toxic cyanobacteria for risk application in freshwater environments

Fanny HERARD

BIOSENSOR PRINCIPLE

The biosensor is a genus-specific ribosomal RNA/DNA probe-based assay for potentially toxic genera and is calibrated to equivalent cell biovolume.

The biosensor device for detecting toxic cyanobacteria is based on a sandwich hybridization assay, which utilizes a capture probe to capture target RNA from a complex environmental sample. The captured RNA is then detected by a signal probe that is coupled to a colorimetric detection.

Basically, the signal intensity obtained from the assay is proportional to the amount of RNA applied to the biosensor, which is a proxy of the biovolume of a target cyanobacteria genus.

SAMPLING CAMPAIGN

The sampling campaigns took place in August 2018 at the Santillana reservoir in Spain. Twice a week, 1,2 L were filtered through 1 µm polycarbonate filters, which were kept in 2 mL RNA lysis buffer (Zymo Research) and immediately send to Microbia Environnement for cyanobacteria detection. All the samples were extracted and then diluted in order to have an equivalent volume of 100, 50, 25, 10 and 5mL. This step is very important to find the linear phase and then eliminate outliers or points in the stationary phase. This prevents the under-estimation of the sample biovolume and elucidates the biosensor saturation.

During this campaign, 14 samples were fixed with Lugol for taxonomic analysis of cyanobacteria species by CIMERA and they were all compared to colorimetric detection.

For more information about our services and products: contact@microbiaenvironnement.com

MATERIAL & METHODS

RESULTS

Pictures of Microcystis aeruginosa (A), M.wesenbergii (B) and bloom of Microcystis in Santillana reservoir (C)

Here the field validation results, performed in Santillana reservoir concerning the detection of Microcystis genera during the summer 2018 campaign, are presented. The samples were tested for the presence of Microcystis genera while the different cyanobacteria species were counted by microscopic analysis (CIMERA). Results were qualitatively and quantitatively compared with microscope counting.

CONCLUSIONS

The preliminary data showed a good agreement between biovolumes estimated via microscopy and colorimetric biosensor results. Fourteen samples have been tested in this study, more data are needed to statistically validate the biosensor. Currently, two other sampling campaigns are in progress (nautical base of Champs-sur-Marne ; lakes in Aquitaine Region). These data will help us to tune sensitivity, reproducibility and robustness of the biosensor.

METHODS COMPARISON

Santillana Reservoir, Spain

Data show that results are qualitatively comparable where the increase of biovolumes during the sampling period are correlated to the increase of biosensor absorbance signal.

1* , Stéphane SAUVAGERE 1 , Adel EL ANJOUMI EL AMRANI 3 , Delphine GUILLEBAULT¹, Christophe LAPLACE-TREYTURE 2 , Sylvia MOREIRA 2 , Álvaro ORTEGA PIZARRO 3 , José Miguel RODRÍGUEZ CRISTÓBAL 3 , Carmem-Lara MANES 4 ¹ Microbia Environnement, Banyuls sur Mer, France ² IRSTEA, UR EABX, 50 avenue de Verdun, F-33612 Cestas Cedex, France 3 CIMERA, Calle de Santiago Grisolía, 2, 28760 Tres Cantos, Madrid, Espagne 4 present address: 41 rue Camille Desmoulins, Banyuls sur Mer, France


N Hernández-Sánchez

L Lleó

B Diezma

F Ammari

T R Cuadrado

P Barreiro

J M Roger

M Ruiz-Altisent

Front-face and right-angle fluorescence spectroscopy for monitoring EVOO evolution

Keywords: Oil quality, oil storage, oxidation, principal component analysis

Fluorescence spectroscopic techniques have been applied in a study in order to monitor the evolution of the spectral pattern of Extra Virgin Olive Oil (EVOO) samples exposed to indirect light at room temperature. Detailed information has been extracted from three dimensional Front-Face (FF) fluorescence spectra with excitation wavelengths ranging from 230 to 646 nm and emission wavelengths ranging from 250 to 698.5 nm. Relevant emission regions have been revealed in FF experiments that have been useful to study the variability of the characteristic spectral patterns and to develop fast inspection procedures. Such procedures include simultaneous excitation at the wavelengths lower than 400 nm, which has been proposed and implemented in a right-angle prototype in order to monitor the evolution of EVOO samples exposed to indirect light. Emission signals at local maxima around 400 nm, 434 nm, 464 nm, 513 nm and 674 nm are found relevant. Ongoing research highlights that hyperspectral images provide spectral patterns of the olives allowing a more precise sorting into categories, which would enable the classification into lots of oils with more homogeneous characteristics for subsequent modelling.

INTRODUCTION

The olive oil is spreading all over the world promoted by the sensorial attributes and health benefits associated with the chemical and nutritional composition of the extra virgin olive oil (EVOO) [START_REF] López-Miranda | Olive oil and health: Summary of the II international conference on olive oil and health consensus report, Jaén and Córdoba (Spain) 200[END_REF]. Under oxidation inducing conditions promoted by illumination, a sample of olive oil might contain a plethora of oxidation compounds. Most of the olive oil components involved in the oxidation processes, such as chlorophyll, antioxidants compounds (polyphenols and α-tocopherol), and primary and secondary oxidation products are fluorescent molecules [START_REF] Sikorska | Analysis of Olive Oils by Fluorescence Spectroscopy: Methods and Applications, Olive Oil -Constituents, Quality, Health Properties and Bioconversions[END_REF]. Thus, a complex fluorescence spectral pattern is expected to reveal the oxidation status. Fast inspection procedures are of great interest. Hence, identification of the bands of excitation wavelengths providing information on oxidation evolution is a major issue to develop a fast procedure with simultaneous excitation that give rise to the useful emission signals.

The present work shows the preliminary results of a study where detailed regions of excitation-emission wavelengths have been extracted from three dimensional front-face (FF) fluorescence spectra. Simultaneous excitation at the wavelengths of interest has been proposed and implemented in a right-angle (RA) prototype in order to monitor the evolution of EVOO samples exposed to indirect light.

The hyperspectral imaging system allows integrating spectroscopic and imaging techniques to enable direct identification of different components or quality characteristics and their spatial distribution within the tested samples [START_REF] Lara | Monitoring spinach shelf-life with hyperspectral image through packaging films[END_REF][START_REF] Lleó | Comparison of multispectral indexes extracted from hyperspectral for the assessment of fruit ripening[END_REF]. Hyperspectral imaging techniques have emerged as a powerful technique in agricultural and food systems as they overcome the limits of spectroscopic techniques and vision techniques, they. Hyperspectral images could provide detailed spectral patterns useful to characterize olives and to sort them into homogeneous categories previous to oil extraction.

MATERIALS AND METHODS

Samples

Set 1 included two EVOO samples directly obtained from producers (P1 and P2) and five EVOO samples (according to commercial labelling) acquired from retail markets (C1, C2, C3, C6 and C8); and seven olive oil (OO#) samples from retail markets (Table 1). 3D FF and RA fluorescence spectra were obtained from each sample, and then, analyzed and compared in order to assess the most suitable configuration of the right-angle experiments. Set 2 included thirteen EVOO samples, according to commercial labelling, obtained from retail markets. Commercial samples (C#) included monovarietal oils, known varietal blends and unknown varietal blends (Table 2). These samples were exposed to indirect light in transparent glass bottles at room temperature during five months. RA fluorescence spectra were monthly acquired from each sample from the control month at the beginning of the experiment to the fifth month under treatment. Principal component analysis (PCA) was performed on centred data. Set 3 included olive fruits samples, cultivar Picual, visually sorted into group 1 (G1: green and 'pintonas' olives), G2 (purple olives) and G3 (black olives).

Three-dimensional front-face fluorescence spectroscopy

Three-dimensional front-face (3D FF) fluorescence spectra were measured using a spectrofluorometer (LS45, Perkin-Elmer) equipped with a Xenon lamp source, excitation and emission monochromators and a front-face sample-cell holder.

The excitation wavelengths ranged from 230 to 646 nm (step 4 nm) and the emission wavelengths ranged from 250 to 698.5 nm (step 0.5 nm). Excitation and emission monochromator slit widths were set at 10 nm. Quartz cuvettes (10 mm × 10 mm × 45 mm) were filled with the oil samples without prior preparation.

For each olive oil sample a rectangular area was selected that included the region of oxidation products. The excitation band ranged from 274 nm to 400 nm and the emission band ranged from 375 to 550 nm. The coefficient of variation (CV) of the signal intensity was computed at every pair (exi,emj) among the samples in both the EVOO group and the OO group.

For each excitation wavelength, the excitation spectrum was obtained as the sum of the signal along the selected emission wavelengths. The emission spectrum was obtained as the sum of the signal along the selected excitation wavelengths for each emission wavelength. Univariate and multivariate analyses were developed in order to identify the emission wavelengths with potential information related to K270. The univariate approach consisted of the computation of the coefficient of determination R 2 between the signal intensity at each individual wavelength from 275 to 550 nm and the analytical K270 values. The multivariate approach computed the b-coefficients of a partial least square regression (PLSR) for the whole wavelengths range. Performance was evaluated through R 2 and the bias corrected standard error of prediction (SEPc).

Right-angle fluorescence spectroscopy

The RA fluorescence spectra were obtained using a photonic multi-channel spectrometer (Hamamatsu, Japan) with detection wavelengths ranging from 196.9 nm to 958.8 nm (step 0.75 nm). A UV-VIS light source (L10290, Hamamatsu, Japan) was used with a Deuterium lamp, with spectral range from 200 to 400 nm. Integration time was set to 1400 ms and 3 repetitions per measurements were averaged.

A prototype with a right-angle sample-cell holder was designed and assembled by LPF-TAGRALIA. Two optical filters were coupled to the setup so as to constrain both the excitation and the emission wavelength ranges. The optical filter coupled with the fiber coming from light source allowed a beam with wavelengths below 400 nm. The other optical filter limits the registered beam to wavelengths higher than 400 nm. Measurements were carried out using quartz cuvettes (10 mm × 10 mm × 45 mm) directly on the oil samples without prior preparation.

Hyperspectral vision

The hyperspectral vision system consists of a push-broom CCD camera (Andor Luca) equipped with a spectrograph Headwall Photonics HyperspecTM VNIR (400 to 1000 nm, 189 wavelengths, spectral resolution 3.17 nm). The illumination was provided by two halogens lamps. The platform moved under the camera and the image was scanned line by line according to the movement (push-broom system). The spatial resolution was 260 µm. Once the raw images were acquired, the corresponding relative reflectance hypercube was computed, containing the relative reflectance spectrum of each pixel of the image with respect to a reference (mean spectrum of a barium sulfate white reference). Standard Normal Variate and Savitsky Golay were pre-processing techniques applied to the spectra.

RESULTS AND DISCUSSION

Fluorescence spectroscopy

For each olive oil sample of Set 1 a rectangular area was selected that included the region of oxidation products. The excitation band ranged from 274 nm to 400 nm and the emission band ranged from 375 to 550 nm. Examples are shown in Figure 1.

The CV(%) of the signal intensity was computed at every pair (exi,emj) among the samples in the corresponding group. For EVOO samples the region with the highest CV(%) values appeared within exi from 274 to 318 nm and emj from 380 to 490 nm, i.e. CV higher than 25% up to 40% (red and dark yellow in Figure 1). Smaller regions showed CV around 25% (light yellow in Figure 1) and a large region showed values around 15% (light blue in Figure 1). For OO samples higher CV values were found than for EVOO samples. Most of the points within the selected area showed CV higher than 30% (Figure 1). Excitation wavelengths around 280, 325 and 345 nm provided wide emission ranges with the highest CV values, around 35%.

According to that, the sources of information on the oxidation conditions probably arise at least from these three excitation spectral regions. Therefore, a more complete evaluation relies on the registration of the signal resulting of wide bands of excitation wavelengths. When fast monitoring is additionally required, the optimal solution is to implement a simultaneous excitation at those wavelengths. Author-produced version of the article published in Acta Horticulturae, 2018, N°1199, p. 497-504. The original publication is available at https://www.actahort.org/books/1199/1199_79.htm Doi: 10.17660/ActaHortic.2018.1199.79

Another approach was considered by simulating a simultaneous excitation by summing the emission signal along the excitation wavelengths. Two spectral analyses, univariate and multivariate, were developed on emission spectra in order to identify the emission wavelengths with potential information related to K270. Olive oil (OO) samples were included so as to increase the range of K270 values.

For the EVOO group low R 2 values were obtained, below 0.4, along the whole range. Despite that, two relevant wavelengths can be identified as corresponding to spectral peaks, i.e. around 375 nm and 443.5 nm, the former with the highest R 2 (Figure 2 upper left). As for the OO group, R 2 values higher than 0.7 were obtained at 400 nm and subsequent wavelengths for the whole range. When the grand group was analysed (EVOO and OO), R 2 reacheed values above 0.9 for all the wavelengths, with a maximum of 0.98 around 445 nm (Figure 2 upper left). The high R 2 for all the emission wavelengths suggests that an intense oxidation produces a general increase of the signal along the whole spectrum, which is related to the overall increase of K270 value. Signal from secondary oxidation products seems to dominate the whole spectra, probably concealing other signals.

The multivariate approach identifies a specific emission band ranging from 375 to 450 nm that provides higher b-coefficients, with a maximum around 410 nm (Figure 2 upper right). The R 2 was 0.94 and the SEPc was 0.0364 using one latent variable.

In Figure 2 (bottom left) the intensity of the emission signal at 400 nm is depicted versus the measured K270 values. In Figure 2 (bottom right) similar result is obtained when multivariate prediction is illustrated by measured K270 values versus predicted K270 values for one latent variable.

The obtained results are similar to those found by previous works where a single excitation wavelength was used for excitation. Kyridiakis and Skarkalis (2000) used 365 nm as excitation wavelength and found a R 2 of 0.96 between emission at 445 nm and K270. [START_REF] Guzmán | Evaluation of the overall quality of olive oil using fluorescence spectroscopy[END_REF] found the best performance by applying PLSR with six latent variables for excitation wavelength at 380 nm. Emission spectra ranged from 300 to 800 nm. The root mean square prediction error was 0.08 and the R 2 was 0.92 obtained with the external validation.

However, in the present work a broad band of excitation wavelengths is studied with the aim of capturing signal arising from wider variety of compounds.

In view of these results, the signal arising from most of the fluorophores related to the oxidation processes could be obtained with excitation at wavelengths below 400 nm. The emission signals would be related to oxidation by-products and chlorophylls. A wide range of excitation would allow capturing different compounds undergoing production and degradation processes. Therefore, subsequent analyses focused on a simultaneous excitation of olive oil samples exposed to indirect light in a RA prototype.

From two months onwards, samples became a light yellowish/yellowish-brown fluid with solid material deposited at the bottom of the containers and minor variation in the spectral pattern was detected (results not shown). At control month spectra were dominated by the signals from chlorophyll at around 670 nm and the shoulder at 725 nm (Figure 3). After two months, considerable decrease was observed. Opposite evolution was observed for the peaks within the region of oxidation products. Noticeable increase was detected for peaks with local maxima at around 400 nm, 434-437 nm, 464-469 nm, and 510-518 nm (Figure 3). Such peaks were in accordance with those found in FF experiments. PC1 loadings in Figure 3 illustrate the variability. Modelling this evolution with regard to official EVOO samples is the following stage that will be faced.

Hyperspectral vision

Spectral patterns were established for each maturity stage according to increasing levels of ripening stage: G1 green olives, G1 'pintonas' olives, G2 purple olives, G3 black olives (Figure 4). Green spectra showed a relevant absorption band at 680 nm, corresponding to high chlorophyll content, and high reflectance level at 550 nm, probably related to low anthocyanin content. Average spectrum of 'pintonas' olives and of purple olives, showed similar pattern. It seems that these two types of spectra belonged to the same ripening level. These spectra also showed an absorption band at 680 nm, with lower intensity in comparison to green olives spectra. In addition the intensity of reflectance at 550 nm is lower probably due to higher anthocyanin content. 'Pintonas' olives showed therefore two types of spectra, two different levels of ripening stage within the surface of the fruit. Hyperspectral images are able to sense these differences in comparison to classical spectroscopy measurements, reflecting non-homogeneities of ripening within the surface of the fruit. Finally black spectra showed no absorption band at 680 and no reflectance at 550 nm, suggesting a total degradation of the chlorophyll and high content of anthocyanins respectively.

CONCLUSIONS

The 3D front-face spectra reveal detailed excitation-emission regions. The coefficient of variation of the signal highlights broad regions related to oxidation processes of olive oil.

The emission band from 375 to 550 nm includes signals produced by an excitation band from 274 nm to 400 nm that shows correlation to the characteristic K240, which is identified with advanced oxidation status. Univariate and multivariate approaches provide correlation with this single parameter.

Simultaneous excitation with a wide range of wavelengths below 400 nm in rightangle experiments is a fast procedure to obtain signals arising from most of the fluorophores related to the complex oxidation processes. The pattern of the spectra acquired using a right-angle device is in accordance with the pattern of the spectra obtained by integrating the emission signal in FF spectra, showing similar positions of the emission peaks. The right-angle emission spectral pattern of olive oils exposed to illumination evolves to lower chlorophyll signal and to higher signal of oxidation by-products.

Ongoing research highlights that hyperspectral images provide spectral patterns of the olives allowing a more precise sorting into categories, which would enable the classification into lots of oils with more homogeneous characteristics for subsequent modelling.

Figure 1 .

 1 Figure 1. Upper left: Example of raw signal from 3D FF spectrum of an EVOO sample. Bottom left: Coefficient of variation (%) for the group of EVOO samples. Upper right: Example of raw signal from 3D FF spectrum of an OO sample. Bottom right: Coefficient of variation (%) for the group of OO samples. Note different colour scales.

Figure 2 .

 2 Figure 2. Upper left: coefficient of determination R 2 between the signal intensity at each individual wavelength from 275 to 550 nm and the analytical K270 values. Bottom left: intensity of the sum of emission signal at 400 nm versus measured K270 values. Upper right: b-coefficients of a PLSR. Bottom right: measured K270 values versus predicted K270 values by a PLSR (one latent variable) (SEPc, bias corrected standard error of prediction).

Figure 3 .

 3 Figure 3. Right-angle fluorescence emission spectra of the commercial EVOO samples. Left: at the control month. Middle: after two months exposed to indirect light. Right: loadings of the first principal component (96.52% explained variance).

Figure 4 .

 4 Figure 4. Left: Example of images of olives samples in false colour, merging three colour planes: blue, 450 nm; green, 550; red, 680 nm. Right: Whole set of pre-processed spectra (N=13500); average spectrum of each maturity class is plotted.

Table 1 .

 1 Characteristics of the extra virgin olive oil and olive oil samples of Set 1 analyzed by 3D front-face and right-angle fluorescence spectroscopy.

	EVOO Samples	P1	P2	C1	C2	C3	C6	C8
							Blend:
							Hojiblanca
	Cultivar	Arbequina	Picual	Arbequina	Hojiblanca	Picual	Arbequina	Arbequina
							Picual
							Cornicabra
	Origin	Producers	Producers	Retail market	Retail market	Retail market	Retail market	Retail market
	K270	0.11	0.12	0.12	0.13	0.14	0.14	0.13
	OO Samples	OO1	OO2	OO3	OO4	OO5	OO6	OO7
	Origin	Retail market	Retail market	Retail market	Retail market	Retail market	Retail market	Retail market
	K270	0.28	0.34	0.41	0.41	0.41	0.48	0.50

Table 2 .

 2 Characteristics of the EVOO samples of Set 2 obtained at retail markets analyzed by right-angle fluorescence spectroscopy. (*, analyzed by 3D FF).

	Sample	C1 *	C2 *	C3 *	C4	C5	C6 *
							Blend:
							Hojiblanca
	Cultivar	Arbequina	Hojiblanca	Picual	Blend	Blend	Arbequina
							Picual
							Cornicabra
	Sample	C7	C8 *	C9	C10	C11	C12	C13
		Blend:
		Hojiblanca
	Cultivar	Arbequina	Arbequina	Blend	Blend	Blend	Blend	Blend
		Picual
		Cornicabra

Author-produced version of the article published in Acta Horticulturae, 2018, N°1199, p. 497-504. The original publication is available at https://www.actahort.org/books/1199/1199_79.htmDoi: 10.17660/ActaHortic.2018.1199.79

ACKNOWLEDGMENTS

Authors gratefully acknowledge the Centro Tecnológico Agroalimentario de Lugo (Spain), Comunidad de Madrid (S2013/ABI-2747, TAVS-CM, Spain) and European Structural Funds for financial support. In addition authors gratefully acknowledge Juan Ramón Izquierdo from the Laboratorio Arbitral Agroalimentario of MAGRAMA (Spain) for expertise advice on olive oil quality, and for providing samples and analytical data. LPF-TAGRALIA is part of the CEI Moncloa Campus of Excellence, UPM-UCM (Spain).