Food Flavors

N. Cayot1

INTRODUCTION

The flavor of food products is one of the most important characteristics determining the choice by the consumer. Flavors are perceived through different ways: odors may be perceived before the consumer can see the product or at the opening of the food package. The product is then chewed, modified by saliva (dilution, enzymatic modification), modified by mouth temperature, and ingested. All these steps will modify the release of flavor compounds. Flavor perception is thus a multi-step and multi-factor process. During food manufacturing, both raw materials and process are contributing to the flavor profile of a final food product. Moreover the addition of exogenous flavoring substances is of common use in food manufacturing. In fact, food flavors are needed:

-to formulate a product with given sensory expectations. For example, the addition of cocoa in the paste gives a chocolate flavor to the cake. -to hide some off-flavors (non-expected flavors). For example, vanilla can be added to soy products to hide the beany flavor of soya in a dessert cream. -to enforce endogenous flavors and ensure the regularity of the product.

For example, strawberry flavor can be added to strawberry ice cream and give a regular product whatever the season and the maturity of the strawberries. -to compensate the modification of endogenous flavors occurring during manufacturing process. For example, orange juice is extracted from fresh oranges, concentrated, transported, and then diluted and stabilized to obtain orange juice. Orange flavor can be added to compensate flavor loss and modification during process.

Food flavors added to products are submitted to an EU regulation. This regulation is based on a common authorization procedure for food additives, food enzymes and food flavorings (Regulation (EC) No 1334/2008(OJ L 354, 2008). Additionally there is a positive list for approved flavoring substances; it means that substances that are specifically authorized can be used to the exclusion of other substances.

An expended list of flavoring substances has been adopted on the 1 st October 2012 (Commission Implementing Regulation (EU) No 872/2012 (OJ L 267, 2012). New flavoring substances and certain complex flavoring substances must have an in depth risk assessment. The regulation indicates that flavorings must be used to improve or modify the odor and/or taste of foods for the benefit of the consumer, and not be used in a way as to mislead the consumer.

Flavoring substances are defined chemical substances, which include flavoring substances obtained by chemical synthesis or isolated using chemical processes, and natural flavoring substances. The following definitions are given is the regulation text (Regulation (EC) No 1334/2008(OJ L 354, 2008):

-'flavorings' shall mean products: (i) not intended to be consumed as such, which are added to food in order to impart or modify odor and/or taste; (ii) made or consisting of the following categories: flavoring substances, flavoring preparations, thermal process flavorings, smoke flavorings, flavor precursors or other flavorings or mixtures thereof; -'flavoring substance' shall mean a defined chemical substance with flavoring properties;

-'natural flavoring substance' shall mean a flavoring substance obtained by appropriate physical, enzymatic or microbiological processes from material of vegetable, animal or microbiological origin either in the raw state or after processing for human consumption. Natural flavoring substances correspond to substances that are naturally present and have been identified in nature; -'thermal process flavoring' shall mean a product obtained after heat treatment from a mixture of ingredients not necessarily having flavoring properties themselves, of which at least one contains nitrogen (amino group) and another is a reducing sugar (aldehyde or ketone function) ; many aroma compounds are produced this way by an pool of reactions named Maillard reactions; -'smoke flavoring' shall mean a product obtained by fractionation and purification of a condensed smoke yielding primary smoke condensates, primary tar fractions and/or derived smoke flavorings.

Flavoring preparations used in food manufacturing can be composed of two parts: a flavoring part (from the categories defined here above) and an nonflavoring part containing ingredients, antioxidants, preservatives, stabilizing agents, anticaking agents, water, ethanol, and other support materials.

The non-flavoring part is necessary to ensure good storage of the food flavor and to favor the application in the food product. The flavoring part is mainly composed of volatile chemical compounds able to reach the olfactory epithelium (Fig. 7.1.) and be perceived as odorant. The interactions among all these chemical compounds and also with the other ingredients within the flavoring preparation and within the food products give the final odorant profile of the product.

The aim of this chapter is to give the main keys of understanding of food flavor design and flavor release control.

THERMODYNAMICS RELATED TO FLAVOR INTERACTIONS, PARTITIONING AND KINETICS OF FLAVOR RELEASE

The release of volatile compounds may be described by three steps:

 transport within the food product towards the interface with the gaseous phase,  partition between the food product and the gaseous phase,  transport within the gaseous phase.

Thermodynamic equilibrium

Chemical potential in the gas phase The gas phase above a product is considered as a perfect gas with water and volatile compounds. The chemical potential μ i of a compound named i is written as a function of the partial pressure of i :

0 0 ln P P RT i i i    
where μ i 0 , the reference chemical potential for i in a gaseous state under standard conditions of temperature and pressure; Pi, the partial pressure for i and P 0 , the reference pressure.

Chemical potential in the liquid phase In the liquid phase, the chemical potential varies with the composition of the solution :

i i i i x RT       ln 0
where x i , the molar fraction of i in the liquid phase, γ i , the activity coefficient of i.

For an ideal solution, γ i =1. It means that, chemical species are coexisting with no interaction. It is the case for infinite dilution (the activity coefficient γ i ∞ is then constant. For a real solution, γ i ≠1. γ i > 1, if interactions between solutes and solvent are far lower than the interactions among solutes or among solvent molecules. γ i < 1 in the opposite case.

The vapor-liquid equilibrium For an ideal solution, the partial pressure for i is given by the Raoult law:

i S i i x P P  
where P i S , the saturating pressure vapor of i For a real solution, the equilibrium between phases is given by the Henry law:

i i i H x P   H i is defined as follows: i S i i P H   
If the gas phase is a perfect gas,

T i i P y P  
where y i , the molar fraction of i in the gas phase and P T , the total pressure of the system.

The activity coefficient is then

S i T i i i P P x y   
The ratio y i /x i is the gas-liquid partition coefficient expressed with molar fraction. This thermodynamical parameter stands for the volatility of the compound i. Under conditions of infinite dilution, K i is constant.

From the analysis of the gas phase and the liquid phase at equilibrium, it is then possible to measure the gas-liquid partition coefficient, with molar fraction or weight fraction:

L V i C C K 
Where C V and C L are weight concentrations, respectively in the vapor phase and in the liquid phase (g.l -1 ). When some interactions exist between the compound i and the other constituents of a solution, K i is lower than its value in water. A retention or release ratio can then be calculated:

100    solution solution water K K K R (%)
A positive value corresponds to retention of the volatile compound by the liquid phase; a negative value corresponds to release as compared to pure water.

Mass transport

Transport phenomena within the different phases can be described by two main mechanisms of mass transfer: diffusive transport in motionless phases and convective transport in well-stirred phases.

Diffusive transport

In a macroscopic, motionless (without internal movement and deformation), homogeneous (made up of one phase) and isotropic (properties independent of the direction considered) medium, aroma compounds diffuse in the direction of their decreasing chemical potentials, until thermodynamic equilibrium is reached, i.e., when chemical potentials are the same on both sides of the medium. To solve transfer equations, a simplification is generally made, considering chemical potential as a concentration or partial pressure (in the gas phase).

Diffusion corresponds to mass transfer from one place to another place due to random agitation of molecules. The diffusive transport model is based on Fick equations. Fick's first law applies to a permanent rate of flow or steady state (no accumulation, i.e., no change of concentration with time) with unidirectional diffusion and in the absence of any chemical reaction.

dx dC D A dt dm J i i i i    
where dx dC i , the concentration gradient of i on the distance dx; D i , the diffusion coefficient of i (m 2 ˑs -1 ).

In case of transient flow rates (variation of mass flux with time), Fick's second law, which is a derivative of the first law, applies:

2 2 x C D t C i i i     
where D, the aroma compound diffusion coefficient or diffusivity (m 2 ˑs -1 ); C, the aroma compound concentration (kg.m -3 ).

Diffusion coefficients varying from 10 -5 to 10 -14 m 2 ˑs -1 can be found in scientific literature. The highest values are found for the diffusion of small molecules in air (D = 2ˑ10 -5 m 2 ˑs -1 for water vapor in air).

Mass transfer model (convective transport)

-Mass transfer between a well-stirred fluid (forced convection) and a homogeneous surface In a biphasic system with a well-stirred fluid, while mass transfer occurs between the two phases, the existence of a thin regular boundary layer with constant thickness  and a laminar rate of flow is assumed near the interface, in which mass transfer resistance (1/k) occurs. Outside this thin boundary layer, mass transfers are fast enough for the fluid to be considered homogeneous at a given time and the mean concentration of the diffusing molecule to be considered constant (C 1 ). In these conditions, the rate of aroma compound diffusion is given in the following equation:

x C C D A dx C d D A dt dm J i i i i          ) ( 2 1
The mass transfer coefficient is defined as:

We obtain

) ( 1 2 C C k A dt dm J i     
-Mass transfer between two well-stirred fluids Each phase presents a boundary layer. Mass transfer resistance only occurs in boundary layers. At the interface, both fluid concentrations reach equilibrium.

The partition coefficient K i of compound i between fluid 2 and fluid 1 is defined as follows:

* * 1 2 C C K i 
Mass transfer from fluid 1 to fluid 2 is given by:

x D k     2 1 C K C k A J i app   
where, the apparent mass transfer coefficient :

1 2 1 1 k K k k i app  

Transfers between phases

Partition and transfers occurred between the different phases of a food product all along its shelf life and during food consumption. Different phases can be listed, for example:

 Food and saliva layer on tongue  Food product and gas phase above it in the food package  Different phases of the food product that undergoes modifications and is no more at equilibrium. Volatile compounds move as a function of their affinity for the different phases. Transfers of volatile compounds are often coupled with transfers of other small molecules such as water, coloring molecules, protons… Various factors are affecting release and listed hereafter.

-The properties of volatile compounds For pure compound: chemical class, saturating vapor pressure, melting temperature, molar mass. In a homogeneous phase: solubility, partition coefficient (K), activity coefficient γ i . In a heterogeneous phase: hydrophoby (logP), apparent partition coefficient (K app ). Hydrophoby is often defined as logK between water and n-octanol. It generally varies from -3 to 7. Compounds with logP < 1 can be considered as relatively polar. Negative values correspond to hydrophilic compounds.

-Food products Effects are observed through composition (that can induce specific molar interactions) and structure (that can hinder transport phenomena).

Generally speaking, for interactions between a macromolecule (M) and a ligand (L), the following equilibrium is observed:

ML L M  
Where K a the association parameter.

app k      L M ML K a 
Molecular interactions are due to existing attractive or repulsive forces between groups of molecules whose valences are satisfied.

The attractive forces include several types of interactions: interactions of van der Waals, hydrogen bonds and hydrophobic interactions. They depend in particular on the presence of electric charges and polarity of the various connections involved.

Such interactions may influence the release of volatile compounds, modifying their partition coefficient, and, as a consequence the mass transfer.

Depending on the composition of the food product, volatile compounds can be trapped, absorbed or adsorbed, covalently bound, or encapsulated. The release of volatile compounds is influenced by dry matter, pH, and ionic strength. For instance, the presence of solutes such as salts or sucrose may induce an increase of release known as « salting out » effect.

Food structure may also have an impact on flavor release. Liquid systems are relatively simple to understand considering partition and release. For viscous or solid systems, additional parameters such as diffusion must be taken into account.

More complex structures such as foams and emulsions were also studied.

The diffusion of molecules is inversely proportional to viscosity, as described by the Stockes-Einstein law:

r kT D i   6 
where k, Boltzman parameter; T, temperature (K) ; η, dynamic viscosity of the solution (Pa.s) and r, radius of the molecule (m).

It is noticeable that temperature as a direct influence on diffusion. Is also has a strong influence on volatility (K) [START_REF] Voilley | Diffusivity of volatiles in water in the presence of a third substance[END_REF] :

         RT H A K v exp
where H  , volatility energy ; R, perfect gas parameter, and T, temperature (°K).

When the gas phase is renewed by a gas flow, the concentration of the volatile compound in the gas phase is the result of equilibrium between its release in the gas phase and its extraction by the gas flow.

When the food product is stirred, mass transfers are accelerated. Mastication of the food product also has an impact on the exchange surface and thus modifies the concentration gradient of the volatile compound.

VOLATILE COMPOUNDS AND AROMA COMPOUNDS

Volatile compounds are chemical molecules of small molecular mass (below 250). Being volatile does not mean being odorant. To be odorant, the compound must not only reach the olfactory epithelium, but also interact with the olfactory receptor cells, give a signal transmitted by the olfactory nerve, and finally be interpreted by the brain as an odor.

Physicochemical characteristics of volatile compounds

An odor can be given by one molecule, for example vanilla odor is mainly given by vanillin, but, more frequently, an odor is the result of the action of numerous volatile compounds. The odor of coffee was analyzed to be the results of above 650 compounds coming from coffee beans, compounds produced by the fermentation of coffee beans, and compounds produced by thermal treatments such as roasting [START_REF] Richard | Connaissance de la nature des arômes[END_REF]. In fact, volatile compounds are of various origins (Figure 7.3.1.). Some precursors exist in raw materials and are transformed due to reactions occurring during the product life: oxidations, microbial metabolism, enzymatic reactions, Maillard reactions. Volatile compounds may be produced from fatty acids, from amino acids (Strecker degradation), from acetic acid (to give terpenoides), from sugar (caramelization) or from a combination of reductive sugar and molecules that contain amino groups (Maillard reactions). Autoxidation gives carboxylic acids with short chains, aldehydes, and ketones, which can give a rancid odor. This type of odor is generally described as offflavors.

The result of all these reactions implied in the formation of flavor is that volatile compounds may be from all types of chemical classes. Some physicochemical characteristics of these compounds are important for their use, such as vapor pressure of the pure compound, solubility in water or in other solvents, hydrophobicity (generally given by the partition ratio between octanol and water), and volatility (partition between air and water). Some data are given in Table 7.3.1. to show the wide range of these parameters. These characteristics are the basis to understand how each molecule will behave in a food product and mainly how it will interact with other ingredients. For example, ethyl butyrate is more soluble in water and more volatile than hexanal despite the fact they have the same hydrophobicity.

N. Cayot

Reactivity and storage of volatile compounds

As many volatile compounds are necessary to prepare a formula used to flavor a manufactured food product, a first step is to be able to mix together different volatile compounds in a solvent. Various solvents can be used depending on the final use of the food flavor. Some frequently used solvents are propylene glycol, triacetin, alcohol.

Mixing molecules of different chemical classes together in a solvent allow them to react with each other's. For example, aldehydes such as hexanal and vanillin may interact with alcohol such as propylene glycol to form hemiacetals or acetals (Figure 7.3.2.1.). The formation of such compounds is an equilibrium that depends on pH conditions (notice that food products are mainly acidic). Therefore, a volatile compound can be modified when added to the mixture and give no odor or a different one. Conversely, some new compounds may appear in the mixture. As seen from table 7.3.1., volatile compounds may be of very different solubility and hydrophobicity. Therefore, it is sometimes impossible to store the different compounds in mixture. For example, citrus or orange flavors are often in two separated phases, -one aqueous phase which was obtained from the juice containing for example aldehydes among which acetaldehyde, -one apolar phase which was obtained from the orange peel and which is the bulk of orange essential oils containing terpens such as limonene. [START_REF] Rega | Texture-flavour interactions: how physico-chemical data explain sensory perception in orange juice[END_REF]) Both phases are mixed together in the final product for juices produced with concentrated extracts. Some pure aroma compound may also be in a solid state at ambient conditions. It is the case for vanillin (CAS number: 121-33-5; melting point 81-83°C) that needs to be heated to be solubilized in a liquid flavor formula to be applied during the production of a liquid product, or remain as solid in a powder formula to be applied in a powder mixture.

Another important aspect of reactivity is the sensitivity to oxidation in presence of oxygen. As oxidation is favored by light (with pigments) and temperature, flavor formula must be stored at cold temperature and in the dark. Another way to prevent compounds from oxidation is to use encapsulation.

Perception of volatile/odorant compounds

As a function of their physico-chemical characteristics, volatile compounds will reach more or less easily the olfactory epithelium and be perceived or not as odorant. For each compound, the quantity necessary for it to be detected as odorant can vary a lot. Moreover, higher concentrations are necessary for it to be recognized and named. Some examples of detection thresholds are given in Table 7.3.3.1. Therefore some odorant compounds may be at very low concentration in a product and be the main compound from a sensory point of view.

The sensory descriptor of an odorant compound is not strictly linked with its chemical class. Nevertheless some general classification has been reported and can be useful to have an idea of the expected odor of a mixture. It must be noticed that the sensory descriptor of a pure compound can vary with its concentration, and can become disgusting when too concentrated. For example, 4methoxy-2-methylbutanethiol was reported as very important for blackcurrant odor, but in high concentration was described as "cat pee" [START_REF] Latrasse | L'arôme du cassis (Ribes nigrum L.). Odeur principale et notes secondaires[END_REF]. The easy oxidation of thiol (2R-SH + O 2  R-S-S-R + H 2 O) could be another explanation: the disulfide could smell cat pee.

The formulation of a food flavor, as the formulation of a perfume, is therefore a question of equilibrium between different compounds in an appropriate solvent. Some example of food flavor composition is given in Table 7.3.3.2., showing that slight variations in the composition lead to different overall perceived odor.

Within such a list of compounds, some molecules are more important as others for the overall aroma perceived. They are named "key aroma compounds". Some authors analyzed the aroma of food products using a technique named Gas Chromatography olfactometry (GC-O) and used it with different dilutions of the extract to determine the relative odor potency of the compounds. An odor active value (OAV) is calculated as the ratio of the concentration of the compound to its odor threshold. The odor potency is said to be proportional to the OAV. Such methods allowed the determination of key aroma compounds of various products (W. [START_REF] Grosch | Gas-chromatography-olfactometry of aroma compounds[END_REF]. From (Ziegler, 2008) Beyond these key aroma compounds, the other volatile compounds present in the flavor formula are very useful to give authenticity to the flavor and to make the flavor more subtle. For example, a strawberry flavor composed of 10 major compounds will be perceived as chemical or candy-like strawberry as compared to a strawberry flavor composed of 100 compounds with more subtle notes and that will be perceived as closer to the aroma of "the strawberry of my grandmother's garden' or the aroma of 'the wild strawberry'.

N. Cayot

Gas chromatography-olfactometry is a technique based on sensory evaluation of the eluate from a chromatographic column. The concentration of the extracts used for GCO analysis must be chosen in order to ensure correct detection by instrumental detection and perception by panelists. The column is connected to both a detector (for example, flame ionization detector) and a sniffing port. Humidified air is added via the sniffing port to avoid dehydration of nasal mucosa of the panelists. Panelists are asked to smell the effluent of the column and to give the start time and a verbal description of each perceived odor. The responses of the panelists are analyzed in parallel with the instrumental results. This method has been used to identify key aroma compounds and off-flavors in several food products (W. [START_REF] Grosch | Review. Determination of Potent Odourants in Foods by Aroma Extract Dilution Analysis (AEDA) and Calculation of Odour Activity Values (OAVs)[END_REF]. Different methods can be used to characterize the odor activity of the volatile compounds: detection frequency, dilution to threshold, and direct intensity [START_REF] Delahunty | Gas chromatography olfactometry[END_REF].

IMPACT OF INTERACTIONS, STRUCTURE, AND TEXTURE ON AROMA PERCEPTION

Once the correct equilibrium is obtained in the flavor formula, it must be tested in the food product to be flavored. Given the fact that other ingredients of the food product may interact with each of the volatile compounds, this step is not the easiest to handle.

Interactions between volatile and non-volatile compounds

Lipids, polysaccharides and proteins may all interact with volatile compounds, but in different manners. The result of interactions can be measured on the partition coefficient of the volatile compounds between the vapor phase and the product.

Interactions between volatile compounds and lipids

Lipids contained in food products are mainly composed of triglycerides which can solubilize great amounts of volatile compounds, which are mainly hydrophobic or at least partly hydrophobic compounds. From a chemical point of view, interactions with lipids depend on the composition in fatty acids, the chain length, and the unsaturation rate. The higher the chain length, the lower the solubility of aroma compounds in lipids. The more the lipid is unsaturated, the more the volatile compounds are retained in the lipid fraction. Partition coefficients (vapor / lipid) of some volatile compounds at 25°C are reported in Table 7.4.1.1. 3.0 ˑ10 -5 4.9 ˑ10 -5 2.5 ˑ10 -4

From [START_REF] Meynier | Importance relative des différents constituants d'un aliment sur la volatilité des composés d'arôme[END_REF] Benjamin et al. ( [START_REF] Benjamin | Static headspace analysis of volatile compounds released from β-lactoglobulin-stabilized emulsions determined by the phase ratio variation method[END_REF])) studied the release under equilibrium conditions of volatile compounds from oil-in-water emulsions stabilized by β-lactoglobulin. The effects of lipid type and mass fraction were investigated using soy oil and anhydrous milk fat. Gas-matrix partition coefficients were measured at 40°C by static headspace gas chromatography for five volatile compounds: 1-propanol, ethyl butyrate, heptanal, octanol and 2-decanone. The more hydrophobic volatile compounds were retained to a higher degree in the oil and emulsion systems than in the aqueous solution. The increase in lipid content in the emulsions caused a significant reduction in K values. However, a change in lipid type (did not have such a pronounced effect on the release, except for octanol and 2-decanone which were more strongly retained by the AMF emulsion From [START_REF] Benjamin | Static headspace analysis of volatile compounds released from β-lactoglobulin-stabilized emulsions determined by the phase ratio variation method[END_REF] At this temperature, all the tested lipids are considered liquid. This is important to notice because the physical state of lipids is of great importance ( [START_REF] Relkin | Effect of fat nature and aroma compound hydrophobicity on flavour release from complex food emulsions[END_REF]).

N. Cayot

In fact, viscosity, density and solid fraction at given temperature have an impact on volatile compounds retention and release. At temperatures below their melting point, lipids are crystallizing. The formation of crystals can induce the release of volatile compounds. When eating a fatty food product, lipids containing aroma compounds are melting in the mouth and, thanks to temperature increase and to chewing, aroma compounds can be easily released in the vapor phase and be perceived.

Interactions between volatile compounds and polysaccharides

Among polysaccharides, starches and their derivatives are frequently used in food products. We also found cellulose, galactomannans, agars, carrageenans, alginates, pectins, microbial polysaccharides, gums, and mucilages…All of them are often used in mixture to give well defined viscosity or texture properties. There are widely used as thickener, gelling agent, stabilizer and fat replacer in many products such as dressings, dairy products, and candy products. They are also used to encapsulate aroma compounds and to stabilize flavor for soft drinks. Texturing agents are modifying the viscosity, which induces a significant decrease in perceived flavor above the critical concentration (c*) at which entanglement begins.

However thickened solutions of similar viscosity do not induce the same flavor perception, indicating that both viscosity and binding affect flavor release and perception. Some of the polysaccharides not only have an impact on the physical properties of food but can also induce a drastic decrease of the volatility of aroma compounds.

From this point of view, starch is the most studied polysaccharides. Natural and processed starches occur under various partially crystalline types defined by their X-ray powder diffractograms. The corresponding structures result from different helical conformations and packing arrangements. The ability of starch to interact with some ligands, particularly aroma compounds, has been extensively studied. It may entrap aroma compounds simply by hydrogen bonding with hydroxyl groups of starch. More particularly, the amylose fraction of starch is able to form complexes with hydrophobic compounds of small steric hindrance (Figure 7.4.2.1.2.1.). The so-called V-types are obtained after addition of complexing agents. In fact, amylose is one of the starch polymers. It has a helical structure and is able to form semi-crystalline complexes which are known for their high aroma retention [START_REF] Jouquand | Formation of amylose complexes with C6aroma compounds in starch dispersions and its impact on retention[END_REF]. Depending on the ligand, the structure of V amyloses can be classified into two families: V 6 and V 8 where 6 and 8 represent the number of D-glucosyl units per turn. In V 6 amylose, three types of crystalline packing (V , V 6II and V 6III ) may be obtained depending on the nature of the complexing molecule which can be trapped either in the single helix of amylose (V 6I ) or both within and between amylose helices (V 6II and V 6III ) [START_REF] Helbert | Single crystals of V amylose complexed with n-butanol or n-pentanol: structural features and properties[END_REF]) (Figure 7.4.2.1.2.2.). Some starch derivatives such as maltodextrins and β-cyclodextrins are exhibiting a structure identical to the one of amylose with a hydrophobic core and a hydrophilic external surface. This structure gives them the ability to trap aroma compounds as well. [START_REF] References Arvisenet | Influence of physicochemical interactions between amylose and aroma compounds on the retention of aroma in food-like matrices[END_REF][START_REF] Biais | Structural and stoichiometric studies of complexes between aroma compounds and amylose. Polymorphics transitions and quantification in amorphous and crystalline areas[END_REF] Some part of the aroma compounds trapped by amylose (inclusion complexes) cannot be easily release and thus are lost for perception. Amylases present in saliva can help the release of aroma compounds during chewing of a food product.

In the example used in Figure 7.4.2.1.2.3., the aroma compound is encapsulated using -cyclodextrin, a starch derivate composed of 7 glycosyl units forming a cylinder of internal Ø 0.60-0.65 nm (hydrophobic cavity). The solubility in water at 25°C is 1.85% (w/v). The more hydrophobic the compound, the easier the trapping by -cylcodextrin. [START_REF] Matheis | Raw Materials for Flavourings[END_REF] Figure 7.4.2.1.2.3 allows comparing the oxygen consumption, that means oxidation, of benzaldehyde, a volatile compound with bitter almond odor, as free compound and as encapsulated in -cyclodextrin. It is clear from this example that encapsulation in a good way to slow down oxidation. Encapsulation is also used to obtain a controlled-release of the flavor. For that purpose many biopolymer-based materials can be used, such as Arabic gums, starches, alginates. Proteins (gelatin, casein, soy proteins) and lipids are also used. [START_REF] Van Soest | Encapsulation of fragrances and flavours: a way to control odour and aroma in consumer products[END_REF] Considering polysaccharides others than starch, studies are far less numerous. The impact of the polysaccharides is above all a physical effect on aroma release, i.e. an effect via the modification of the structure and of the texture of the food product.

Interactions between volatile compounds and proteins

The majority of the studies concerning interactions between aroma compounds and proteins were done in aqueous solutions, with varying conditions of pH, ionic strength, and temperature. The most studied protein is β-lactoglobulin, which is a well-known soluble milk protein widely used in the food industry due to its emulsifying properties. Other proteins were also studied, such as caseins and soya proteins.

β-lactoglobulin was reported to interact with a great variety of compounds such as aldehydes, ketones, ionones, and esters [START_REF] Tromelin | Protein-flavour interactions[END_REF]. Within a given homologous series, the affinity of volatile compounds for lactoglobulin is increasing with the length of the aliphatic chain i.e. the hydrophobicity. It is then generally admitted that hydrophobic interactions are existing between volatile compounds and -lactoglobulin. Different binding sites have been identified (Figure 7.4.1.3.) It must also be noticed that aldehydes may form covalent binding with proteins (NH 2 residues). All these interactions or reactions are sensitive to pH because this parameter influences the conformation of the proteins, and, as a consequence, the accessibility of binding sites. For example, an increase of retention of methylketones, esters, and terpens was measured between pH 3 and 9, as compared with conditions between pH 9 and 11 when the protein is denatured.

Studies done with other proteins confirmed that the most hydrophobic molecules were the most retained ones [START_REF] Damodaran | Flavor protein interactions. Binding of carbonyls to bovine serum albumin: thermodynamic and conformational effects[END_REF]. Bovin serum albumin was reported to have high retention capacity [START_REF] Jasinski | Flavor binding by whey proteins[END_REF]. This protein contains 17 cysteyl residues, and is thus very reactive towards sulfur compounds such as allyl isothiocyanate [START_REF] Kawakishi | Interaction of proteins with allyl isothiocyanate[END_REF]. This was also observed in a lesser extent with ovalbumin and lysozyme.

The retention of volatile compounds by soya proteins was found to be intermediate between the one obtained with bovin serum albumin and β-lactoglobulin. One of the soya proteins, β-conglycidin, was reported to have higher retention capacity [START_REF] O'neill | Flavor protein interactions: characteristics of 2nonanone binding to isolated soy protein fractions[END_REF]. To perceive an effective decrease of odor intensity due to protein addition, the retention percentage must be at least of 20%. The formation of covalent binding between proteins and aldehydes can be used to eliminate some off-odors because the resulting chemicals are often odorless [START_REF] Meynier | Importance relative des différents constituants d'un aliment sur la volatilité des composés d'arôme[END_REF].

Effect of structure and texture on diffusion and perception

The understanding of how flavor compounds can interact with other ingredients is indispensable but not sufficient to fully understand what is occurring in a real food product. Food products are mixtures containing all the ingredients cited here above, and moreover these ingredients together with physical treatments lead to a specific structure and a perceived texture of the food.

Food can be suspensions, emulsions, gels, foams or all of them together. Considering the impact of such structures on flavor release, studies have been focused on emulsions and gels.

Diffusion and perception of aroma compounds in gels

In fact, every thickener or gelling agent, if above a given concentration, can induce the decrease of the aroma release by reducing the diffusion of the small molecules in the thickened or gelled system (Figure 7.4.2.1.).

Diffusion and perception of aroma compounds in emulsions

An emulsion is a non-stable mixture of two non-miscible liquids where droplets of one liquid are dispersed in the second liquid (the continuous phase). In food products, the continuous phase is mainly an aqueous phase, and the dispersed phase is mainly an oil phase. The contact surface between both liquids is named interface and contains some surfactants added to stabilize the emulsion. In food products, surfactants are often proteins, lecithin, mono and di-glycerides, and sucro-esters. The perceived aroma compounds i.e. the amont of aroma compounds present in the vapor phase depends on the location of the aroma compounds in the emulsion. The relative concentrations of aroma compounds within the different phases of the emulsion (dispersed phase, continuous phase, interface between continuous and dispersed phases, interface between the emulsion and the vapor phase) depend on the nature of the different phase, on the physical state, and on their affinity for aroma compounds. The higher the volume fraction of the oil phase in the emulsion, the more important the retention of hydrophobic aroma compounds in the emulsion. Because of their interactions with aroma compounds, the proteins located at the oil/water interface will slow down the transfer of aroma compounds from oil phase to water phase and, as a consequence, slow down the transfer from the emulsion to the vapor phase [START_REF] Charles | Flavor release from salad dressings: Sensory and physicochemical approaches in relation with the structure[END_REF].

If the size of the droplets is decreasing, the surface of the interface is increasing. [START_REF] Mcclements | Food emulsions. Principles, practice, and techniques[END_REF] reported a theoretical relationship between the volatility of an aroma compound and the size of the droplets in the emulsion. Above a 10 μm diameter, partition coefficient remains constant. This theoretical relationship is hardly verified experimentally due to variations of behavior from a volatile compound to another.

Diffusion and perception of encapsulated aroma compounds

To prevent aroma compounds from oxidation or from other types of degradation, they can be encapsulated. Various techniques are employed to form the capsules, including different types of drying, coating, liposome entrapment, coacervation, inclusion complexation… Fats, starches, dextrins, alginates, protein and lipid materials can be employed as encapsulating materials. Many patents are existing using various matrices to entrap volatile compounds. For example:

-Formation of spheres of a solid suspension from a solid fat matrix and a non-fat solid substrate with an active ingredient -Use of an oil-in-water emulsion from a vegetable oil, an aqueous medium and a water soluble carbohydrate film forming agent. The oil-in-water emulsion is sprayed onto a soluble beverage powder, whereupon the aqueous layer of each droplet evaporates to form capsules. The soluble beverage is dissolved in hot water to release the aroma flavor. -Another method for encapsulating volatile aroma compounds involves dissolving an inert gas in an aromatized edible liquid, then co-extruding the gas-liquid mixture with a molten carbohydrate material. The result is a continuous stream of carbohydrate outer shell surrounding an inner core of aromatized, gasified liquid. When these capsules, which can be incorporated directly into an instant consumer product, contact hot water the inert gas in the inner core expands and ruptures the capsule wallreleasing the aromatized liquid. -More simple process are also used such as aroma solution mixed with a carbohydrate matrix, and then dried (for example, vanilla sugar).

In such systems, the aroma compounds are protected when kept in correct storage conditions. The use in food product implies heating or hydration that will allow aroma release. The aroma delivery can then be controlled, depending on the solubility or melting of the capsule walls. Here again, thermodynamics can be used to understand aroma release.

FLAVORING AND FOOD PROCESS: MAIN FACTORS TO TAKE INTO ACCOUNT

Once you know the different types of interactions that can occur and the role of food structure, several questions are remaining: -What about interactions in complex systems? What about competitions among flavor compounds and towards other ingredients? -When is the best moment to flavor the product? Is there an order to follow to put the ingredients in the food formula?

To answer this last question, you also have to consider the process the product will undergo with its different operating units.

In fact food manufacturing process generally comprises operating units such as mixing, thermal treatments to remove water or to stabilize the product, particle size modification, and dilution. Important parameters are modified during these steps: temperature, pressure, water content and activity, gas composition. All of them are of great impact on volatile compounds behavior, as shown before.

Finally, the phenomena occurring must be taken into account throughout the lifetime of the product from manufacturing to consumption, and throughout storage.

Some applications of such principles and reasoning can be evoked through the production of an orange juice from concentrate.

Juice from concentrate used squeezed fruit juice as raw material, to which about 80% of its original water has been removed. The concentrate thus obtained is easier to store and transport. Arrived at the place of bottling, the juice is reconstituted with the same amount of water as the original juice. The legislation authorizes restitution of flavor and pulp lost during the process of concentration, from fruits of origin or from other fruits, but the same variety. A simplified production chart is given in Figure 7.5. Formulation of the orange juice In this product, some aroma compounds are present in the concentrate. They were coming from the juice directly obtained from fruits. This juice underwent centrifugation and pasteurization. Thus some aroma compounds were lost, or oxidized, or modified, due to temperature increase during process that will have an impact on volatility. Other raw materials added in the product (sugars) are poor in volatile compounds but give dry matter that can be a good support for aroma compounds.

Flavoring preparations can be added in a liquid form. For example an orange flavoring with an aqueous phase and a lipid phase is often used. All the volatile compounds will be mixed together. Partition and transfer will be modified.

Limiting aroma modification through the process After mixing of all the ingredients and filtration, the obtained juice is pre-heated. This pre-heating allows the de-gassing of the juice. Indeed, gas will expand when temperature is increasing. This step is interesting to preserve the flavoring that was previously added because oxygen is removed and then further oxidation will be limited.

Nevertheless, some aroma compounds will also be lost because volatility will increase together with temperature. That is why, at this step, the vapor phase is collected by condensation. The vapor phase will contain water but also some volatile compounds, probably the most hydrophilic compounds. The condensate will then be re-introduced in the juice to limit aroma loss. Then pasteurization occurs. In modern production plants, flash pasteurization is used in order to limit the effect of temperature. Thus, Maillard reactions are drastically limited. The aroma compounds produced by Maillard reactions can thus be limited.

A correct storage of the bottled juice must then be observed in order to avoid further degradation of the aroma. Despite all the precautions taken, the two phases of the orange flavoring are often demixing. The separation of the lipid phase from the juice is thus resulting in the apparition of a yellowish annulus at the top of the bottle. In other processes, come pulp can be added to the juice and can be a good support material for the aroma compounds.

KEY FACTS

 Raw materials and process are contributing to the flavor profile of a final food product, but the addition of exogenous flavoring substances is often necessary in manufactured products.  Food flavors added to products are submitted to EU regulation (1331/2008/EU).  Flavoring preparations can be composed of two parts: a flavoring part and a non-flavoring part to ensure correct application and storage.  Volatile compounds are chemical molecules of small molecular mass.  To be odorant, the volatile compound must not only reach the olfactory epithelium, but also interact with the olfactory receptor cells, give a signal transmitted by the olfactory nerve, and finally be interpreted by the brain as an odor.  An odor is generally the result of the action of numerous volatile compounds.  Volatile compounds are of various origins and of all types of chemical classes.  The formulation of a food flavor is a question of equilibrium between different compounds in an appropriate solvent.  Lipids, above all in liquid state, can solubilize great amounts of volatile compounds.  The amylose fraction of starch is able to form complexes with hydrophobic compounds of small steric hindrance which are thus lost for perception; the impact of other polysaccharides is an effect via the modification of the structure and of the texture of the food product.  The most hydrophobic volatile molecules were the most retained ones by the proteins; aldehydes may form covalent binding with proteins.

N. Cayot

 Every thickener or gelling agent, if above a given concentration, can induce the decrease of the aroma release by reducing the diffusion of the small molecules in the thickened or gelled system.  The perceived aroma compounds above an emulsion depend on the location of the aroma compounds in the emulsion.  Important parameters are modified during food process: temperature, pressure, water content and activity, gas composition. All of them are of great impact on volatile compounds behavior.

EXERCISES

1. Vanilla aroma is produced via two different processes. Process A is an extraction of the vanilla beans with ethanol / water; Process B is a bio-conversion of lignin by a microorganism. Which processes give 'natural flavoring substance' as defined by the EU regulation? Explain why.

2. The physicochemical characteristics of two volatile compounds are given:

-Isoamyl Acetate (MW= 130 g.mol -1 ; log P = 2.13; P i s at 25°C = 733 Pa; solubility in water at 25°C = 2.4 g.L -1 ) -Linalool (MW= 154 g.mol -1 ; log P = 3.54; P i s at 25°C = 27 Pa; solubility in water at 25°C = 2.6 g.L -1 ). Which compound is the most volatile? Which one is the most hydrophobic?

3. The composition of a flavoring formula is given in the following 4. Observe and comment the following figure

Release of aroma compounds from different oils 5. Observe this figure and explain why aroma diffusion is less important in gels than in solutions.

6. You want to make a food product with a reduced amount of fat. What will occur with flavor compounds? What strategy will you adopt to keep flavor in the product?

SOLUTIONS

1. Both processes give 'natural flavoring substance' as defined by the EU regulation i.e. flavoring substance obtained by appropriate physical, enzymatic or microbiological processes from material of vegetable.

2. Isoamyl Acetate is the most volatile compound. Linalool is the most hydrophobic compound.

3. The great amount of esters probably gives a fruity odor.

4. The release of both aroma compounds is decreasing as the unsaturation rate of the oil is increasing. At a given temperature, the higher the unsaturation rate, the more important the liquid fraction of the oil. Thus, for higher rates of unsaturation, the oil is able to solubilize higher amounts of aroma compounds which are then more retained.

5. The diffusion coefficient of ethyl butyrate is varying with the nature of the matrix. It is high in water, lower in a gelled product because the mobility of molecules is lowered in a structured media. The presence of high amount of sucrose also decreases a lot the mobility of molecules because the main role of water is then to solubilize sucrose. The lowest diffusion coefficient is observed for a gelled matrix containing high amount of sucrose.

6. When food products are made with a reduced amount of fat, aroma compounds are less solubilized in the product. Then aroma loss and deterioration can easily occur, and the food product is less odorant when melting in the mouth. This will be particularly the case for the most hydrophobic compounds of the flavor formula. The common strategy used is to try to compensate this phenomenon by using other means to trap aroma such as entrapment in polysaccharides and/or by changing the aroma profile using less hydrophobic compounds. The moment of addition of the flavoring during the process can also be adapted to ensure better preservation of the aroma.

Figure 7 . 1 .

 71 Figure 7.1. How volatile compounds can reach the olfactory epithelium? An odor is generally composed of many different chemical compounds. The names used for the odor compounds are sometimes not clear. Commercial labels are used together with chemical names that may correspond to different nomenclatures. It is thus preferable to use CAS numbers or FLAVIS numbers given in the EU regulation to avoid mix-up.The interactions among all these chemical compounds and also with the other ingredients within the flavoring preparation and within the food products give the final odorant profile of the product.

Figure 7

 7 Figure 7.2.2. Mass transfer between well-stirred fluid and homogeneous surface.

Figure

  Figure 7.3.1. Biogenesis of aroma compounds: main simplified pathways

Figure 7

 7 Figure 7.3.2.1. Reactivity of volatile compounds in mixture. Example of vanillin in a strawberry flavor formula[START_REF] Savary | Impact de la structure de gels composites à base de polyosides sur la libération d'arôme : exemple des bases de fruits-sur-sucre[END_REF].

Figure

  Figure 7.3.3.1. Classification of odor as a function of chemical class

Figure 7

 7 Figure 7.3.3.2. Gas-Chromatography-olfactometry: principle of the method

Figure

  Figure 7.4.2.1.2.1. Structure of the amylose helical chain

Figure 7

 7 Figure 7.4.2.1.2.2. Different type of interactions of volatile compounds with starch and amylose (Arvisenet, Le Bail,[START_REF] References Arvisenet | Influence of physicochemical interactions between amylose and aroma compounds on the retention of aroma in food-like matrices[END_REF][START_REF] Biais | Structural and stoichiometric studies of complexes between aroma compounds and amylose. Polymorphics transitions and quantification in amorphous and crystalline areas[END_REF]

Figure

  Figure 7.4.2.1.2.3. The role of encapsulation to prevent oxidation of volatile compounds. Example of benzaldehyde (CAS number 1477-19-6).[START_REF] Matheis | Raw Materials for Flavourings[END_REF]

  Figure 7.4.1.3. Tertiary structure of β-lactoglobulin with trapping of isoamyl acetate in the hydrophobic cavity

Figure

  Figure 7.4.2.1.: The effect of the state of the matrix on mass transfer. Typical values of D, the diffusion coefficient (10m 2 /s) of flavor compounds in different matrices.

Figure 7 . 5 .

 75 Figure 7.5. Simplified process for the production of fruit juice from concentrate.

Table 7 .

 7 3.1. Some usual volatile compounds and their physicochemical characteristics.

		Solubility					Vapor
	Compounds	in (25°C) water	Solubility (others)	Log water	P oct-	pressure (25°C)	K (30°C) air-water
		g/L					Pa
			Miscible
	Ethyl-butyrate 5.65	alcohol,		1.80		1885	1.83. 10 -2
			ether
			1g/	4ml
	Furaneol	< 10%	ethanol at	0.82		-
			95%
	Cis-3-hexenol <1%			1.61		-	5.36 10 -4
	Vanillin	10			1.21		0.003
	Hexanal	2.60			1.80		1420-1517	1.16 10 -2
	-ionone	very low			3.57		-
	(Savary, Doublier, & Cayot, 2005)

Table 7 .

 7 3.3.1. Some examples of detection thresholds of odorant compounds

	Volatile compounds	1 g in …m 3	concentration
	Ethanol	0.01	1•10 -4
	Bis(2-methyl-4,5-dihydrofuran-3-yl)disulfur	50	2• 10 -8
	-ionone	250	4• 10 -9
	Nor-isoborneol	29000	3.5• 10 -11
	Geosmine	67000	1.5• 10 -11
	-ionone	140000	7• 10 -12
	Methyl-1-bicyclo[3,3,0]-dithia-2,4-oxa-8-octan 2500000	4• 10 -13
	Trichloro-2,4,6-anisol	33000000	3• 10 -14

Table 7 .

 7 3.3.2. Example of food flavor formula

	odor	Apple	Banana	Pear	Pineapple
	1-butanol	30	5	30	1
	2-methyl butanol	50	5	50	5
	1-Hexanol	30	5	40	1
	Amyl acetate	0	50	10	20
	Isoamyl actetate	5	150	5	5
	Ethyl butyrate	5	40	10	10
	Amyl butyrate	5	30	20	20
	Heptyl acetate	5	5	100	5
	Ethyl-2-methyl butyrate	5	10	5	20
	Allyl caproate	5	5	5	120
	Citronellyl acetate	5	5	40	1
	Hexanal	100	1	5	1
	(E)-2-hexenal	100	10	30	5
	Benzaldehyde	0.1	0.2	0.2	0.1
	Vanillin	1	30	1	30
	Eugenol	0.1	2	0.2	0.1
	Ethanol	693.8	686.8	638.6	770.8
	total	1000	1000	1000	1000

Table 7

 7

	.4.1.1. Partition coefficients (vapor / lipid) of some volatile compounds in various
		lipids at 25°C
	Type of lipid	Ethyl butyrate	Ethyl hexanoate
	Triolein	2.2 ˑ10 -4
	Tricaprin	2.1 ˑ10 -4
	Sunflower oil	8.5 ˑ10 -3

Table 7

 7

	.4.1.2. Partition coefficients (vapor / liquid, K x 10 3 ) of some volatile compounds in
	aqueous solution, oil, and 40% o/w emulsion stabilized by 1% -lactoglobulin at 40°C
	Volatile compound	Log P	solution Aqueous	Oil	Emulsion
	1-Propanol Ethyl butyrate Heptanal Octanol 2-Decanone	0.25 1.85 2.29 3.00 3.73	0.71 17.74 29.30 5.65 50.87	9.89 1.00 0.60 0.31 0.05	1.41 1.53 0.59 0.47 0.61

  table. What can you tell about the most probable sensory descriptors of this formula?

	CAS No.	Name	Amount [mg/g]
	3658-77-3	Furaneol	5
	121-33-5	Vanillin	5
	103-26-4	Methyl Cinnamate	24
	123-66-0	Ethyl Hexanoate	20
	105-54-4	Ethyl Butyrate	90
	140-11-4	Benzyl Acetate	2
	93-92-5	Styrallyl Acetate	1
	706-14-9	gamma-Decalactone	20
	134-20-3	Methyl Anthranilate	1
	108-64-5	Ethyl iso-Pentanoate	10
	66-25-1	Hexanal	1
	3681-71-8	cis-3-Hexenyl Acetate	5
	928-96-1	cis-3-Hexenol	15
	24851-98-7	Methyl Dihydrojasmonate	5
	14901-07-6	beta-Ionone	1
	102-76-1	Triacetin (Solvent)	795
		SUM	1000

Équipe Procédés Alimentaires et Physico-Chimie, UMR A 02.102 PAM AgroSup Dijon/Université de Bourgogne, 1 esplanade Erasme, F-21000 Dijon


Sandy Pagès-Hélary

Laurence Dujourdy
email: laurence.dujourdy@agrosupdijon.fr

Nathalie Cayot
email: nathalie.cayot@agrosupdijon.fr

Natalia Drabi

Ben De Lacy Costello

Identification of Volatile Compounds in Blackcurrant Berries: Differences among Cultivars

Keywords: blackcurrant berries, cultivars, volatile compounds, GC-MS, SPME, multivariate statistical analyses, chemical profiling

Berries of blackcurrant are known to produce a strong flavor. Some previous studies have reported that a given cultivar of blackcurrant can produce berries with a specific profile of volatile compounds. For the Burgundy region in France, the Noir de Bourgogne cultivar is especially important because it is the main ingredient of a liquor with a designation of origin. The aim of the present study was to characterize the volatile fractions of berries from 15 cultivars in order to explore the possibility of using different cultivars for liquor production. The plants were cultivated under the same conditions and harvested in the same year. The volatile fractions of the harvested berries were analyzed using HS-SPME-GC-MS. Thorough univariate statistical analysis and multivariate analysis were applied to the dataset, which made it possible to identify groups within cultivars. The Rosenthal cultivar exhibited a quite flat profile; the Lositkia, Ben Tiran, and Barchatnaia cultivars shared common features; the Noir de Bourgogne cultivar showed the highest amounts of molecules such as 3-carene, limonene, β-phellandrene, ocimene, α-terpinolene, and bicyclogermacrene. None of the studied varieties were close to the Noir de Bourgogne on the basis of VOC analysis.

Introduction

Blackcurrant berries (Ribes nigrum L.) are cultivated in different countries around the world for diverse uses; for instance, as perfumes, jams, or juices or as coloring matter or an ingredient for alcoholic beverages [START_REF] Cortez | Ribes nigrum): A Review on Chemistry, Processing, and Health Benefits[END_REF]. In France, blackcurrant is mainly cultivated in four different areas: Val de Loire, Oise, Vallée du Rhône, and Burgundy. For the Burgundy region, blackcurrant is especially important because it is the main ingredient of blackcurrant liquor, which is an emblem of this area. The liquors cassis de Dijon and cassis de Bourgogne both hold designations of origin. In order to get these designations of origin [2,3], liquors have to be produced mainly from the Noir de Bourgogne cultivar, which is responsible for the special taste of these liquors.

The particular organoleptic quality of the Noir de Bourgogne cultivar is empirically well-known but there are limited data in the literature to demonstrate this, except for the publication by Latrasse et al. [4].

In fact, Latrasse et al. [4] studied the volatiles of a blackcurrant juice distilled under vacuum and of hydro-alcoholic infusions. They reported that the Noir de Bourgogne cultivar was particularly rich in diacetyle, ethyl butyrate, and eucalyptol. No other data have been further reported for this cultivar. The more recent studies have been published on other cultivars used throughout the world; for example, the Brödtrop cultivar was used in the study by Liu et al. [5] and found to be characterized mainly by ethyl acetate, aromadendrene, p-cymen-8-ol, 3-methyl-1-butanol, and cis-3-hexen-1-ol. The cultivars Mortti, Ola, and Melahati were used in the study by Marsoll-Vall et al. [START_REF] Marsol-Vall | Profiles of Volatile Compounds in Blackcurrant (Ribes nigrum) Cultivars with a Special Focus on the Influence of Growth Latitude and Weather Conditions[END_REF], who reported monoterpene hydrocarbons and oxygenated monoterpenes as the major classes of volatiles.

The volatile fraction of blackcurrant berries is known as being sensitive to the cultivar, but it is also influenced by the climate, the area of growth, and processes like freezing or juice extraction [START_REF] Marsol-Vall | Effects of processing and storage conditions on volatile composition and odor characteristics of blackcurrant (Ribes nigrum) juices[END_REF]. Therefore, great attention should be paid to these parameters and to experimental conditions when comparing the volatile fractions of different cultivars.

The present study focused on the volatile fraction of blackcurrant berries from different cultivars, including Noir de Bourgogne. The aim was to characterize the volatile fractions and compare them to that of the Noir de Bourgogne cultivar in order to explore the possibility of using other cultivars for the liquor production. The substitution would be possible if a cultivar showed organoleptic qualities close to those of Noir de Bourgogne and better resistance to pests and climate changes.

The similarities and differences among the volatile fractions of the blackcurrant berries from fifteen different cultivars (including Noir de Bourgogne), cultivated under the same conditions (same area, same treatment, etc.) and harvested in the same year, were studied. Odorant molecules from all the different berries were collected and identified using HS-SPME coupled with GC-MS. The whole dataset was analyzed with different statistical methods in order to determine differences. The objective was to obtain chemical profiles for the volatile organic compounds (VOC) of the blackcurrant berries of the different cultivars.

Results and Discussion

Volatile Compounds Representative of the 15 Cultivars

Molecules detected and identified in the headspace for the berries from the 15 cultivars are listed in Table 1.

Only three compounds were badly chromatographed due to co-elution, but identification was reliably achieved by probability-based matching with mass spectra in an MS library:

•

For retention index 1203, the peak was attributed to β-phellandrene;

•

For retention index 1256, the peak was attributed to ocimene;

•

For retention index 1622, the peak was attributed to aromadendrene.

Additionally, three compounds could not be identified and were named NI1, NI2, and NI3 (respectively, retention indexes 1162, 1587, and 1825).

Other than these volatiles, 31 compounds were clearly identified, among which 26 were already reported in the scientific literature. Thus, 34 compounds were retained, which are reported in Table 1.

The profiles for volatile compounds varied greatly among the different cultivars, and they could be used to associate a specific odor with the corresponding berries.

For example, Figure 1 shows the mean of the normalized abundance of the 34 targeted compounds for four cultivars showing very different profiles, among which is Noir de Bourgogne (NBSAY20). Limonene, β-phellandrene, ocimene and bicyclogermacrene were more abundant in Noir de Bourgogne than in the three other cultivars. Descriptors were taken from www.thegoodscentscompany.com and www.pherobase.com, (accessed on 17 September 2021). The odor descriptors of these compounds were respectively citrus, mint/herbal, floral, and green/woody/weedy. The chemical profile for the Ben Tiran (BTISAY20) cultivar had the highest abundances of α-terpinolene, caryophyllene, α-cayophyllene, germacrene D, and citronellol. The odor descriptors of these compounds were respectively woody/pine/citrus, woody/clove, woody, wood/spice, and floral/rose bud/citrus.

In contrast, the VOC profiles of the Barchatnaia (BARSAY20) and Lositkia (LOSSAY20) cultivars were quite flat except for sabinene, described as woody/citrus/pine/spice.

Data Sorting

From the chromatographic study, a dataset with 45 observations (15 cultivars × 3 replicates) and 34 variables (peak areas of detected volatile compounds) was obtained.

Prior to the establishment of a GC volatile fingerprint, the reproducibility of the peak areas was checked using the relative standard deviation (RSD). This was calculated from triplicates by multiplying the standard deviation by 100 and dividing by the mean value of the peak areas (given in Table 1).

Two types of RSD were calculated and named RSD "between" and RSD "within". The RSD "between" corresponded to the variation coefficient between different cultivars. It made it possible to determine whether there were huge or small differences between cultivars for each volatile compound.

In contrast, the VOC profiles of the Barchatnaia (BARSAY20) and Lositkia (LOS-SAY20) cultivars were quite flat except for sabinene, described as woody/citrus/pine/spice.

Data Sorting

From the chromatographic study, a dataset with 45 observations (15 cultivars × 3 replicates) and 34 variables (peak areas of detected volatile compounds) was obtained.

Prior to the establishment of a GC volatile fingerprint, the reproducibility of the peak areas was checked using the relative standard deviation (RSD). This was calculated from triplicates by multiplying the standard deviation by 100 and dividing by the mean value of the peak areas (given in Table 1).

Two types of RSD were calculated and named RSD "between" and RSD "within". The RSD "between" corresponded to the variation coefficient between different cultivars. It made it possible to determine whether there were huge or small differences between cultivars for each volatile compound.

The RSD "within" corresponded to the variation coefficient between replicates of a given sample (= cultivar) for each molecule. It made it possible to determine whether the peak area of a given molecule was repeatable or not. These calculations helped to target molecules that were "stable" when similar chemical profiles were analyzed.

The RSD provided information on the extent of variation of a compound. The rarity of compounds, calculated using the frequency of appearance (presence or absence in the samples) in a dataset, made it possible to evaluate discriminating power. Indeed, if a compound was present only in a particular cultivar then it was considered typical of this cultivar (Table 1).

The RSD "within" and RSD "between" are reported in Table 2 for each compound and each cultivar. The extent of variation in a peak area (characterizing its stability) was based on the following assumption. On one hand, the variability within the peak area was considered high if the RSD "within" was above 40%. This number was chosen because of The RSD "within" corresponded to the variation coefficient between replicates of a given sample (=cultivar) for each molecule. It made it possible to determine whether the peak area of a given molecule was repeatable or not. These calculations helped to target molecules that were "stable" when similar chemical profiles were analyzed.

The RSD provided information on the extent of variation of a compound. The rarity of compounds, calculated using the frequency of appearance (presence or absence in the samples) in a dataset, made it possible to evaluate discriminating power. Indeed, if a compound was present only in a particular cultivar then it was considered typical of this cultivar (Table 1).

The RSD "within" and RSD "between" are reported in Table 2 for each compound and each cultivar. The extent of variation in a peak area (characterizing its stability) was based on the following assumption. On one hand, the variability within the peak area was considered high if the RSD "within" was above 40%. This number was chosen because of the complex matrix and the difficulty of integrating the peak. It meant that the chromatography of the compound was not reproducible. With such an assumption, ethanol, methyl butanoate, and ethyl butanoate were eliminated from the list of target compounds.

On the other hand, if the RSD "between" was below 70% (chosen for the same reason as for the RSD "within"), this meant that the compound did not vary sufficiently between samples to be able to differentiate them by cultivar. With such a rule, camphene, αphellandrene, NI1, hexanol, NI3, and p-cymen-8-ol were eliminated. In parallel, we checked that there were no outliers. The RSD "between" values were examined in conjunction with the frequency of the appearance of target compounds between different samples. If a peak was rarely present and its area had a high variability, the compound was considered as discriminant. For example, camphene had a low appearance frequency (33%), an RSD "within" far lower than the limit [START_REF] Thioulouse | Multivariate Analysis of Ecological Data with ade4[END_REF], and an RSD "between" value of 69. Therefore, it was kept in the list of target molecules. At the end of this stage, we decided to eliminate only five molecules: ethanol, methyl butanoate, ethyl butanoate, NI1, and hexanol. Molecules with means for the RSD "within" and the RSD "between" colored in black were suppressed for the final multivariate data analysis. Cells colored in gray correspond to molecules with abundances below the detection threshold or molecules found only once among the three replicates.

Then, chemical profiles of cultivars were obtained with 29 target molecules and an exploratory statistical study was implemented. In order to further reduce this number of target compounds, an initial PCA was undertaken, and γ-elemene and NI3 could be eliminated. We also checked that these compounds had tangent RSD values.

Finally, descriptive analysis (univariate and multivariate) was implemented with the 27 remaining target compounds.

Differences among Cultivars Using Univariate Descriptive Analysis

Data visualization was used to find patterns within complex datasets with many features. An UpSet plot (Figure 2) [START_REF] Lex | UpSet: Visualization of Intersecting Sets[END_REF] was used to group data points that had many of the same values across different features together and find the largest intersecting sets. The plot has several parts.

Differences among Cultivars Using Univariate Descriptive Analysis

Data visualization was used to find patterns within complex datasets with many features. An UpSet plot (Figure 2) [START_REF] Lex | UpSet: Visualization of Intersecting Sets[END_REF] was used to group data points that had many of the same values across different features together and find the largest intersecting sets. The plot has several parts. The central bar chart (Figure 2a) shows the number of compounds in the data that were held in common in the cultivars. Each bar indicates a different combination. Underneath the bar chart, a graphical table (Figure 2b) shows which cultivars the different combinations of compounds concerned. Each row indicates one of the fifteen cultivars. The black dots and lines show the combinations of cultivars that made up subsets of compounds. Reading from left to right, all cultivars had 12 compounds in common. The corresponding compounds are in the table above the bar chart (Figure 2c). The molecules showing a frequency of presence of molecule (FPM) of 15 in a cultivar were the ones present in all cultivars: α-pinene, β-pinene, 3-carene, limonene, β-phellandrene, ocimene, αterpinene, 4-aromadendrene, bornyl acetate, caryophyllene, aromadendrene, and α-caryophyllene. The smaller bar chart (Figure 2d) shows the unconditional frequency count (quantity of molecules (QMC)) of each cultivar across all subsets. It can be observed that the highest value corresponded to the Royal de Naples and the Barchatnaia cultivars, The central bar chart (Figure 2a) shows the number of compounds in the data that were held in common in the cultivars. Each bar indicates a different combination. Underneath the bar chart, a graphical table (Figure 2b) shows which cultivars the different combinations of compounds concerned. Each row indicates one of the fifteen cultivars. The black dots and lines show the combinations of cultivars that made up subsets of compounds. Reading from left to right, all cultivars had 12 compounds in common. The corresponding compounds are in the table above the bar chart (Figure 2c). The molecules showing a frequency of presence of molecule (FPM) of 15 in a cultivar were the ones present in all cultivars: α-pinene, β-pinene, 3-carene, limonene, β-phellandrene, ocimene, α-terpinene, 4-aromadendrene, bornyl acetate, caryophyllene, aromadendrene, and α-caryophyllene. The smaller bar chart (Figure 2d) shows the unconditional frequency count (quantity of molecules (QMC)) of each cultivar across all subsets. It can be observed that the highest value corresponded to the Royal de Naples and the Barchatnaia cultivars, which had all the target compounds, and the lowest value was that of the Andega cultivar with 20 compounds. The Noir de Bourgogne cultivar had 25 compounds, including 12 in common with the other cultivars. All the other compounds of the Noir de Bourgogne cultivar were also found in at least seven other cultivars in the present study. The Noir de Bourgogne cultivar presented a medium number of molecules compared to the other cultivars and was not characterized by specific molecules.

A two-dimensional table plot [START_REF] Thioulouse | Multivariate Analysis of Ecological Data with ade4[END_REF] with a proportional circles for the peak area of each compound is shown in Figure 3a (raw data). Missing values were replaced by the detection limit value, set to 50,000 (corresponding to 100,000 divided by 2 g of sample).

The cultivars Noir de Bourgogne and Ben Tiran presented compounds with larger circles than other cultivars. Some molecules were more intense for the Noir de Bourgogne cultivar: 3-carene, limonene, β-phellandrene, ocimene, NI2, and bicyclogermacrene.

Differences among Cultivars Using Multivariate Descriptive Analysis

In order to explore in more depth the chemical profiles and determine the existence of specific groups, we first used principal components analysis (PCA) on normalized data (each peak area was divided by the sum of the areas of all peaks), as a flexible tool. The values for the total soluble solids (°Brix) of the blackcurrant berries, which were not obtained from the GC-MS analysis, were added as supplementary data (Appendix A) in order to visualize the relationship of this variable with the set of active variables via the factorial axes.

The PCA with correlations and a mapping of molecules and cultivars is shown in Figure 4. The cultivars Noir de Bourgogne and Ben Tiran presented compounds with larger circles than other cultivars. Some molecules were more intense for the Noir de Bourgogne cultivar: 3-carene, limonene, β-phellandrene, ocimene, NI2, and bicyclogermacrene. Figure 3b presents the chemical profile of the Noir de Bourgogne cultivar with relative abundances indicated by the circles. This figure underlines the importance of 3-carene, ocimene, and α-terpinolene in the fingerprint of the Noir de Bourgogne cultivar.

Differences among Cultivars Using Multivariate Descriptive Analysis

In order to explore in more depth the chemical profiles and determine the existence of specific groups, we first used principal components analysis (PCA) on normalized data (each peak area was divided by the sum of the areas of all peaks), as a flexible tool. The values for the total soluble solids ( • Brix) of the blackcurrant berries, which were not obtained from the GC-MS analysis, were added as additional data (Appendix A) in order to visualize the relationship of this variable with the set of active variables via the factorial axes.

The PCA with correlations and a mapping of molecules and cultivars is shown in Figure 4.

Three dimensions accounted for 80.7% of the total variance. The contributions for the different molecules highlighted in Figure 3 for the Noir de Bourgogne cultivar were checked: limonene was well-represented in dimension 2, and 3-carene and α-terpinolene were well-represented in dimension 1. Ocimene, NI2, and bicyclogermacrene were wellrepresented in the plan Dim 1-Dim 3. The Brix degree had a poor representation; its impact was low compared to the blackcurrant composition in volatiles. It did not explain the differences observed between varieties of blackcurrant by itself.

Different groups of cultivars could be observed in the Dim 1-Dim 2 plan. The Barchatnaia, Lositkia, and Ben Tiran cultivars formed three independent groups. In the Dim 1-Dim 3 plan, the Noir de Bourgogne and Ben Tiran cultivars were opposed on axis 3. Noir de Bourgogne was characterized by high peak areas for the group of variables comprising β-phellandrene, limonene, aromadendrene, bicyclogermacrene, and ocimene, while Ben Tiran had high peak areas for α-caryophyllene, germacrene D, and methyl salicylate. Noir de Bourgogne had a unique profile and no other cultivar could be grouped with it.

MDS was carried out using Euclidean distances (see Appendix A, Figure A1a) and a distance associated with a Pearson correlation coefficient d = 1cor (see Appendix A, Figure A1b). From the joint analysis of both graphs, we concluded that the Noir de Bourgogne cultivar was opposed to the Lositkia cultivar, and the Rosenthal cultivar was opposed to the Ben Tiran cultivar. Finally, the Barchatnaia and the Lositkia cultivars shared a common feature: high peak areas for Camphene, Sabinene, Linalool, and Bornyl Acetate.

The Lositkia, Ben Tiran, and Barchatnaia cultivars showed quite rich profiles (26 or 27 molecules) and had 25 molecules in common. Some of them were intense (high peak areas) and the Ben Tiran cultivar exhibited the most intense profile. Three dimensions accounted for 80.7% of the total variance. The contributions different molecules highlighted in Figure 3 for the Noir de Bourgogne cultivar checked: limonene was well-represented in dimension 2, and 3-carene and α-terpin were well-represented in dimension 1. Ocimene, NI2, and bicyclogermacrene were represented in the plan Dim 1-Dim 3. The Brix degree had a poor representation; pact was low compared to the blackcurrant composition in volatiles. It did not expla differences observed between varieties of blackcurrant by itself.

Different groups of cultivars could be observed in the Dim1-Dim2 plan. Th chatnaia, Lositkia, and Ben Tiran cultivars formed three independent groups. Dim1-Dim3 plan, the Noir de Bourgogne and Ben Tiran cultivars were opposed o 3. Noir de Bourgogne was characterized by high peak areas for the group of var comprising β-phellandrene, limonene, aromadendrene, bicyclogermacrene, and oci while Ben Tiran had high peak areas for α-caryophyllene, germacrene D, and meth licylate. Noir de Bourgogne had a unique profile and no other cultivar could be gr with it.

Materials and Methods

Cultivars and Cultivation

Fifteen cultivars of blackcurrant (Table 3) were grown in an open field on an experimental plot located in Burgundy (France, 47 • 14 6.508 N, 5 • 6 28.096 E), as shown in Figure 5a. For each cultivar, three plants were cultivated and the plot was organized as described in Figure 5b.

All plants were about six years old and were pruned each year in winter around January (only dead and obstructing branches were cut). Each blackcurrant plant received fertilizers (nitrogen, potassium, magnesium, and phosphorus) but not watering or chemical weeding.

Table 3. List of the cultivars and parameters of cultivation. Size of berries: "heterogeneous" means that berries were of different sizes and "homogeneous" means that berries were globally the same size; maturity of berries: "heterogeneous" means that the maturity and color of berries varied a little and "homogeneous" means all berries were deep black. All plants were about six years old and were pruned each year in winter around January (only dead and obstructing branches were cut). Each blackcurrant plant received fertilizers (nitrogen, potassium, magnesium, and phosphorus) but not watering or chemical weeding.

Cultivars

Berry Harvesting and Storage

The fifteen cultivars of blackcurrant were harvested at maturity. Three conditions had to be met for the fruit to be considered ripe: the berries had to be easy to pick, they had to color the skin when crushed between two fingers, and the total soluble solids concentration ( • Brix) at harvest had to be over 15 • . As all the cultivars ripened at different times (early ripening, middle-season ripening, or late ripening), the harvesting period extended over one month from June to the beginning of July, as indicated in Table 3. All the berries of each plant were manually picked and pooled together for the same cultivar. Cultivars gave berries of different sizes, ranging from 31 to 99 g/100 berries, with 38 g/100 berries for Noir de Bourgogne. Berries were stored frozen (-40 • C) until analysis. The duration of storage ranged from one to three months (Table 3).

Analysis of the Volatile Fraction of Berries

HS-SPME-GC-MS

Volatiles were extracted with 1 cm SPME DVB/CAR/PDMS (divi-nylbenzene/carboxen/ polydimethylsiloxane; 50/30 µm) fiber. The fiber (24 Ga 50/30 µm, for manual holder, 3 pK, 57328-U) was purchased from Sigma (Saint-Quentin-Fallavier, France) and used with a manual fiber holder. SPME vials (20 mL, VA201) and septum caps (18-mm caps, 8-mm PTFE/silicon septum, SACA001) were purchased from JASCO (Lisses, France). Exactly 2 g of frozen berries were placed in the vial. They were left to defrost for 30 min at ambient temperature. Through preliminary experiments, the equilibrium step was set to 30 min at ambient temperature and the extraction step when the fiber was exposed to the headspace to 30 min at ambient temperature.

An HP 6890 Series Gas Chromatograph (Waldbronn, Germany) equipped with an HP 5973 Mass Selective Detector (USA) (Quadrupole) was used with a DB-WAX column (30 m × 0.32 mm × 0.25 µm, 123-7032, Agilent, J&W Scientific, Santa Clara, CA, USA) to analyze the compounds of interest. The SPME fiber was de-sorbed and maintained in the injection port at 250 • C for 5 min. The sample was injected with a purge flow of 20 mL/min for 2 min. Helium was used as carrier gas at 1.4 mL/min with a linear velocity of 43 cm/s. The temperature program was isothermal at 40 • C for 10 min, an increase to 100 • C at a rate of 2 • C/min, isothermal at 100 • C for 6 min, an increase to 120 • C at a rate of 4 • C/min, isothermal at 120 • C for 2 min, and then an increase to 240 • C at a rate of 20 • C/min, which was maintained for 5 min. The total run time was 64 min. The ionization source and transfer line temperatures were set at 230 • C and 160 • C, respectively. The mass spectra were obtained using a mass-selective detector with an electron impact voltage of 70 eV in full scan mode over the range from 29 to 400 m/z.

Analyses were completed in triplicate.

Identification of Compounds

To conduct the data analysis, compounds with areas above 100,000 (TIC intensity corresponding to the peak integration threshold) and known to be odorant were kept.

Identification was based on the analysis of pure compounds: 3-carene, γ-terpinene, ocimene, 1S-α-pinene, β-pinene, and caryophyllene. The other volatile compounds were compared using their mass spectra from several libraries (NIST 08 (National Institute of Standards and Technology), WILEY138, and INRAMASS (personal library)). The identification of these compounds was also verified with retention index calculations.

Selection of Representative Volatile Compounds

Compounds identified for the three replicates of the 2020 harvest were pooled. For the final data analysis, we selected the volatile compounds found at least twice in the three replicates of each cultivar.

Compounds were reported in order of decreasing peak area and decreasing frequency of appearance (which was the percentage for the presence of peaks in the chromatogram).

Comparisons between the different cultivars were based on the 34 compounds with the most important peak areas and highest frequencies of appearance (Table 1).

Strategy for Multivariate Data Analyses

The relative standard deviation (RSD) was used to determine whether the "regular" standard deviation was small or large when compared to the mean for a considered dataset. If percentages were large-for instance, more than 55%-this indicated that the data were more spread out. This made it possible to appreciate how precise the data were in the experiment. The more precise the data, the smaller the RSD was.

Univariate statistical analysis and data visualization were used to produce visualizations of information in order to highlight complex data relationships.

Finally, multivariate data analyses were used to explore chemical profiles in more depth and to determine the existence of specific groups. The goal of these analyses was to extract the important information from the data and to express this information as a set of summary indices called principal components. Then, multidimensional scaling (MDS) was used. This dimension-reduction technique was designed to project high-dimensional data down to two dimensions while preserving relative distances between observations. For both MDS and PCA, proximity measures such as the correlation coefficient or Euclidean distance were used to generate a spatial configuration (map) of points in multidimensional space, where the distances between points reflected the similarity of isolates. PCA minimized dimensions, preserving the covariance of data, and MDS minimized dimensions, preserving the distance between data points. MDS was most useful when the observations were significant and relatively small, such as different cultivars [START_REF] Härdle | Applied Multivariate Statistical Analysis[END_REF][START_REF] Baccini | Stratégies pour l'analyse statistique de données transcriptomiques[END_REF].

Data analyses were carried out using RStudio-1.4.1106 and R-4.0.5 [START_REF] Rstudio_Team | RStudio: Integrated Development for R; RStudio[END_REF][START_REF] R_Core_Team | A Language and Environment for Statistical Computing[END_REF] and specific packages: FactoMineR [START_REF] Lê | FactoMineR: An R package for multivariate analysis[END_REF], Factoextra [START_REF] Kassambara | Factoextra: Extract and Visualize the Results of Multivariate Data Analyses[END_REF], magrittr [START_REF] Bache | A Forward-Pipe Operator for R, R package Version 2[END_REF], dplyr [START_REF] Wickham | Dplyr: A Grammar of Data Manipulation[END_REF], ggpubr [START_REF] Kassambara | Ggplot2 Based Publication Ready Plots, R package version 0[END_REF], corrplot [START_REF] Wei | corrplot": Visualization of a Correlation Matrix[END_REF], UpSetR [START_REF] Gehlenborg | UpSetR: A More Scalable Alternative to Venn and Euler Diagrams for Visualizing Intersecting Sets, R package Version 1[END_REF], reshape2 [START_REF] Wickham | Reshaping Data with the reshape Package[END_REF]. FactoMineR, factoextra, and corrplot were used for PCA; UpSetR and reshape2 for creating UpSet plots; and magrittr, dplyr, and ggpubr for MDS.

Conclusions

Among the 15 cultivars that were cultivated in the same conditions and harvested in the same year, some cultivars showed VOC profiles for their berries that were different from the others:

•

The Rosenthal cultivar exhibited a quite flat profile with 21 molecules and was characterized by sabinene, β-phellandrene, and ocimene;

•

The cultivars Lositkia, Ben Tiran, and Barchatnaia shared the same characteristics, including having the greatest number of detected molecules;

•

The Noir de Bourgogne cultivar did not show specific molecules, but it generally had the highest amounts of several molecules, such as 3-carene, limonene, β-phellandrene, ocimene, NI2, α-terpinolene, and bicyclogermacrene.

No other cultivar in this study seemed able to replace Noir de Bourgogne for the production of liquors. This study should be completed by sensory analyses.

Additionally, it is necessary to study other cultivars or to implement hybridization with Noir de Bourgogne as one of the parents.

Figure 1 .

 1 Figure 1. Graphical representation of the main odorant volatiles extracted from berries of different cultivars.

Figure 1 .

 1 Figure 1. Graphical representation of the main odorant volatiles extracted from berries of different cultivars.

Figure 2 .

 2 Figure 2. UpSet plot showing co-occurrence of volatile molecules in the various cultivars. QMC: quantity of molecules found in each cultivar. The total size of each set is represented on the left barplot (a). Every possible intersection is represented by the bottom plot (b), and their occurrence is shown on the top barplot (c). (d) Detailed table with frequency of presence of molecules (FPM) in cultivars; presence is indicated by 1 and absence by 0.

Figure 2 .

 2 Figure 2. UpSet plot showing co-occurrence of volatile molecules in the various cultivars. QMC: Quantity of molecules found in each cultivar. The total size of each set is represented on the left barplot (a). Every possible intersection is represented by the bottom plot (b), and their occurrence is shown on the top barplot (c). (d) Detailed table with frequency of presence of molecules (FPM) in cultivars; presence is indicated by 1 and absence by 0.

Figure 3 .

 3 Figure 3. Two-dimensional table plot with proportional circles for peak areas of each volatile compound: (a) raw data and (b) relative abundance.

Figure 3 .

 3 Figure 3. Two-dimensional table plot with proportional circles for peak areas of each volatile compound: (a) raw data and (b) relative abundance.

Molecules 2021 , 15 Figure 4 .

 2021154 Figure 4. Cont.

Figure 4 .

 4 Figure 4. Final PCA plot released for harvest data from 2020 for the molecules retained after RSD sorting and the first PC (Car indicates 3Car and Aromad indicates 4Aromad). The color gradient represents the level of square cosines for th variables (a,b) and the individuals (c,d). Variables with poor representation (cos 2 < 0.5) are invisible. The supplementar variable, the Brix degree, is in dashed blue; (a,b) correspond to the variable plots in plans Dim 1-Dim 2 and Dim 1-Dim 3, respectively; (c,d) correspond to the individual plots in plans Dim 1-Dim 2 and Dim 1-Dim 3, respectively.

Figure 4 .

 4 Figure 4. Final PCA plot released for harvest data from 2020 for the molecules retained after RSD sorting and the first PCA (Car indicates 3Car and Aromad indicates 4Aromad). The color gradient represents the level of square cosines for the variables (a,b) and the individuals (c,d). Variables with poor representation (cos 2 < 0.5) are invisible. The supplementary variable, the Brix degree, is in dashed blue; (a,b) correspond to the variable plots in plans Dim 1-Dim 2 and Dim 1-Dim 3, respectively; (c,d) correspond to the individual plots in plans Dim 1-Dim 2 and Dim 1-Dim 3, respectively.

Figure 5 .

 5 Figure 5. Cultivars and growing areas. (a) Aerial view of the Sayens site of Agronov, Agricultural Chamber of Burgundy, Bretenières, France. The red circle indicates the plot where the fifteen blackcurrant cultivars were grown; (b) the organization of the plot. "Cultivars X" refers to other cultivars from the Sayens collection that were not taken into account in the present study.

Figure 5 .

 5 Figure 5. Cultivars and growing areas. (a) Aerial view of the Sayens site of Agronov, Agricultural Chamber of Burgundy, Bretenières, France. The red circle indicates the plot where the fifteen blackcurrant cultivars were grown; (b) the organization of the plot. "Cultivars X" refers to other cultivars from the Sayens collection that were not taken into account in the present study.

Table 1 .

 1 List of volatile compounds detected and identified in the headspace of berries for the 15 different cultivars.

	Retention Index	CAS Number	Molecules	Reported in the Literature	Codes	Occ. Freq.	Mean
	925	64-17-5	Ethanol	[8]	Eth	87	2.98 ×
	975	643-42-7	Methyl butanoate	[9]	Mbut	93	8.53 ×
	1025	80-56-8	α-Pinene	[10]	aPin	100	2.62 ×
	1030	105-54-4	Ethyl butanoate	[4]	Ebut	100	1.45 ×
	1068	79-92-5	Camphene	[11]	Camp	33	9.23 ×
	1102	127-91-3	β-Pinene	[6]	bPin	98	5.86 ×
	1119	3387-41-5	Sabinene	[12]	Sab	96	6.38 ×
	1140	13466-78-9	3-Carene	[13]	3Car	100	2.77 ×
	1154	99-83-2	α-Phellandrene	[11]	aPhe	89	4.79 ×
	1162	99-84-3	NI1		NI1	11	1.33 ×
	1196	138-86-3	Limonene	[4]	Lim	100	4.79 ×
	1203	555-10-2	β-Phellandrene	[9]	bPhe	100	1.06 ×
	1216	6728-26-3	Trans-2-hexenal	[12]	t2hx	82	4.01 ×
	1256	29714-87-2	Ocimene	[10]	Ocim	100	1.91 ×
	1273	586-62-9	α-Terpinolene	[10]	aTerno	100	2.41 ×
	1371	111-27-3	Hexanol	[14]	Hexo	100	6.15 ×
	1513	489-40-7	4-Aromadendrene		4Aromad	98	4.90 ×
	1558	138-87-4	Cis-β-terpineol		cbTerno	78	1.54 ×
	1568	78-70-6	Linalool	[4]	Lin	82	3.65 ×
	1574	76-49-3	Bornyl acetate	[15]	BorA	100	5.25 ×
	1580	87-44-5	Caryophyllene	[9]	Cary	100	1.87 ×
	1587	475-20-7	NI2		NI2	93	8.95 ×
	1622	489-39-4	Aromadendrene		Aro	100	1.25 ×
	1627	339154-91-5	γ-Elemene	[16]	gEle	60	4.91 ×
	1649	6753-98-6	α-Caryophyllene	[13]	aCary	100	4.49 ×
	1671	10208-80-7	Muurolene	[9]	Muu	93	1.06 ×
	1686	23986-74-5	Germacrene D		GermD	87	1.80 ×
	1717	24703-35-3	Bicyclogermacrene	[16]	Bicy	89	1.78 ×
	1760	119-36-8	Methyl salicylate	[13]	Msal	73	4.43 ×
	1789	106-22-9	Citronellol	[9]	Citro	89	8.19 ×
	1825	483-77-2	NI3		NI3	44	4.53 ×
	1848	1197-01-9	p-Cymen-8-ol	[13]	pCym8	73	1.68 ×
	1954	23262-34-2	Dendrolasin		Dend	53	1.42 ×
	2127	6750-60-3	Spathulenol	[12]	Spat	84	2.18 ×
	Codes: abbreviation of each molecule used for statistical analysis; Occ. Freq.: appearance frequency of each molecule for the three replicates
	of each cultivar; Mean: global mean of chromatographic areas, calculated for all cultivars and all replicates, with missing data replaced by
	the normalized chromatograph detection threshold.

Table 2 .

 2 Stability of peak areas, expressed by the relative standard deviation (RSD).

	RSD "Within" for Each Cultivar	NBSAY20	BDSAY20	ANDSAY20	BURSAY20	RNSAY20	LEASAY20	LOSSAY20	OJSAY20	V88SAY20	PCSAY20	BTISAY20	BARSAY20	ROSSAY20	TIBSAY20	TIFSAY20	Mean of the RSD "Within"	RSD "Between"
	Volatile Compounds
	Eth	37	21	54	72	39	30	66	66	53	18		24	37	55			92
	Mbut	59	75	57	15	37	61	42	86	43	23		49	47	86	60		70
	aPin	16	7	4	24	22	61	9	41	61	20	21	19	13	46	64		73
	Ebut	14	52	52	35	31	96	30	76	47	13	15	31	40	71	107		88
	Camp					21		13					29	10				69
	bPin	20	46	4	61	27	67	5	58	23	24	43	62	8	76	61		89
	Sab	16	17	21		59	70	15	53	52	34	24	43	12	68	74		150
	3Car	13	14	6	19	17	65	9	41	17	25	15	35	88	54	56		75
	aPhe	20	7	9	10	27	67	7	23		27	6	21		39	40		49
	NI1
	Lim	8	6	17	11	4	66	9	42	12	31	10	31	13	78	38		84
	bPhe	10	12	8	19	11	50	10	41	18	31	14	36	11	59	52		103
	t2hx		18	37	31	36	87	27	38	24	62	35	17	23		26		86
	Ocim	54	7	14	12	17	59	7	43	14	32	35	40	20	49	48		101
	aTerno	9	10	13	14	15	61	13	39	16	32	11	31	15	58	44		73
	Hexo	11	60	5	15	28	24	33	33	23	99	30	7	12	33	90		52
	4Aromad	35	53	11	25	52	25	2	41	37	10	9	25	17	43	36		84
	cbTerno	36	9			17		43	70	58	1	46	13	15	33			64
	Lin	7	17		23	18	24	18		23	35	11	25		30	14		152
	BorA	11	35	25	9	37	61	15	78	37	33	44	13	17	28	33		152
	Cary	13	10	9	8	22	62	13	95	18	47	13	20	25	51	42		87
	NI2	9	8	13	11	25	63	16	29	45		14	29	22	39			161
	Aro	53	32	23	10	4	62	43	66	23	63	18	12	11	50	8		84
	gEle		12			19	50		47	11	38		17		47	38		70
	aCary	11	9	10	5	18	58	12	40	19	33	10	18	24	44	45		126
	Muu	16	30	6	50	19	75	22	15	64	9	8	17		89	47		114
	GermD	9	14	21	10	23	66	19	59			17	35	10	13	52		135
	Bicy	8	15	11	10	28	65	13	48	30		45	40	28		14		158
	Msal	20				47		40	7	12	51	10	14	24	37	38		139
	Citro	9	28	77	2	24	73	13		16	69	7	25		44	26		114
	NI3	16	12			22				17	43					46		66
	pCym8	21	48		30	24	46	46	57			24	6		23			63
	Dend	11			10	3	31		55			7	25			41		91
	Spat	12	4		9	8	53	13	53	2	25	20	33	20	16			90

Acknowledgments: We thank Philippe Mussillon and Olivier Lenoir for greenhouse growing of blackcurrant plants, Jugurta Bouidghaghen and Cécile Desbois for their help during harvesting, and Olivier Lenoir and Judith Cartron for their advice.

This work was supported by the Regional Council of Bourgogne-Franche Comté and the "Fond Européen agricole pour le développement rural" (FEADER) as part of the project "Pérennité de la filière cassis en Bourgogne".

Institutional Review Board Statement: Not applicable. Figure A1. Multidimensional scaling using (a)

Informed Consent Statement: Not applicable.

Conflicts of Interest:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.