Volatile compounds profiling by using proton transfer reaction‐time of flight‐mass spectrometry (PTR‐ToF‐MS). The case study of dark chocolates organoleptic differences

Deuscher, Zoé; Andriot, Isabelle; Sémon, Étienne; Repoux, Marie; Preys, Sébastien; Roger, Jean‐Michel; Boulanger, Renaud; Labouré, Hélène; Quéré, Jean‐Luc Le

doi:10.1002/jms.4317

Cited by 37 publications

(37 citation statements)

References 154 publications

(376 reference statements)

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“…Their objective was generally to classify the chocolates according to the variety and/or the origin of the transformed cocoa beans by measuring and comparing their relative volatile organic compounds (VOCs) patterns [30,31]. Investigations into the relationships between VOC patterns obtained using a DIMS method and the organoleptic properties of foodstuffs obtained as sensory profiles measured by a panel are rather scarce [32]. Dark chocolates of diverse cocoa origins and cultivars, but manufactured using the same standard process at the pilot level at an industrial plant, could be classified into four sensory categories [32].…”

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“…Investigations into the relationships between VOC patterns obtained using a DIMS method and the organoleptic properties of foodstuffs obtained as sensory profiles measured by a panel are rather scarce [32]. Dark chocolates of diverse cocoa origins and cultivars, but manufactured using the same standard process at the pilot level at an industrial plant, could be classified into four sensory categories [32]. This classification was based on a quantitative descriptive analysis (QDA) protocol conducted by an internal expert panel: The panelists rated 36 flavor attributes, among which 33 were aroma descriptors.…”

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confidence: 99%

“…This classification was based on a quantitative descriptive analysis (QDA) protocol conducted by an internal expert panel: The panelists rated 36 flavor attributes, among which 33 were aroma descriptors. The classification into the four sensory categories is, in this case, the basis of a quality control procedure to define the ultimate use of merchantable cocoa lots as they are received at the factory [32]. In a recent study, we were able to model this sensorial classification by deciphering the volatilome of 206 dark chocolate samples using the DIMS method PTR-MS [32].…”

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“…In a recent study, we were able to model this sensorial classification by deciphering the volatilome of 206 dark chocolate samples using the DIMS method PTR-MS [32]. This approach, combined with chemometrics and variable selection procedures, allowed us to propose a highly significant prediction model of the sensory categories (poles) based on a limited number of ions (10 to 22 depending on the selection method used) [32]. Some of them were tentatively identified as volatile compounds on the basis of their mass formulae determined thanks to the time-of-flight (TOF) mass analyzer used, and literature data [32].…”

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confidence: 99%

“…This approach, combined with chemometrics and variable selection procedures, allowed us to propose a highly significant prediction model of the sensory categories (poles) based on a limited number of ions (10 to 22 depending on the selection method used) [32]. Some of them were tentatively identified as volatile compounds on the basis of their mass formulae determined thanks to the time-of-flight (TOF) mass analyzer used, and literature data [32]. However, some of these ions represented ion fragments and most of the supposedly molecular ions represented many possible isobaric compounds or isomers.…”

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Key Aroma Compounds of Dark Chocolates Differing in Organoleptic Properties: A GC-O Comparative Study

Deuscher

Gourrat

Repoux³

et al. 2020

Molecules

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Dark chocolate samples were previously classified into four sensory categories. The classification was modelled based on volatile compounds analyzed by direct introduction mass spectrometry of the chocolates' headspace. The purpose of the study was to identify the most discriminant odor-active compounds that should characterize the four sensory categories. To address the problem, a gas chromatography-olfactometry (GC-O) study was conducted by 12 assessors using a comparative detection frequency analysis (cDFA) approach on 12 exemplary samples. A nasal impact frequency (NIF) difference threshold combined with a statistical approach (Khi 2 test on k proportions) revealed 38 discriminative key odorants able to differentiate the samples and to characterize the sensory categories. A heatmap emphasized the 19 most discriminant key odorants, among which heterocyclic molecules (furanones, pyranones, lactones, one pyrrole, and one pyrazine) played a prominent role with secondary alcohols, acids, and esters. The initial sensory classes were retrieved using the discriminant key volatiles in a correspondence analysis (CA) and a hierarchical cluster analysis (HCA). Among the 38 discriminant key odorants, although previously identified in cocoa products, 21 were formally described for the first time as key aroma compounds of dark chocolate. Moreover, 13 key odorants were described for the first time in a cocoa product.Molecules 2020, 25, 1809 2 of 32 Thus, quality attributes of dark chocolate produced from Vietnamese cocoa were recently investigated [2], as well as the influence of the cocoa variety on the fermentation step studied [3][4][5], influence of the cocoa origin on the cocoa flavor examined [6][7][8][9], and the link between the origin and process searched for [10]. The process, starting with fermentation [11][12][13][14][15][16] and followed by drying [17] and roasting [18][19][20][21][22][23] steps, has been the subject of many studies. Specialized reviews and treatises gathered such knowledge [1,[24][25][26].Among these investigations, studies aiming to identify the only aroma-active compounds in dark chocolate (the dark chocolate key aroma compounds) are scarce. Nevertheless, some important gas chromatography-olfactometry (GC-O) studies, completed by GC-MS, allowed identification of the major dark chocolate key aroma compounds. Thus, the influence of conching was examined, with the aim of identifying the key odorant compounds in dark chocolate [27]. Aroma-active compounds in dark and milk chocolate in relation to sensory perception were investigated [28], and recently, key aroma compounds in two commercial dark chocolates with high cocoa contents were characterized [29]. As already cited, the major results are reported in reviews on cocoa and cocoa products [24][25][26] as well as in a specialized treatise dedicated to chocolate [1].Direct injection mass spectrometry (DIMS) methods, such as proton transfer reaction mass spectrometry (PTR-MS), have been used in some studies conducted on dark chocolates. Their obje...

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Key Aroma Compounds of Dark Chocolates Differing in Organoleptic Properties: A GC-O Comparative Study

Deuscher

Gourrat

Repoux³

et al. 2020

Molecules

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SO‐CovSel: A novel method for variable selection in a multiblock framework

Biancolillo

Marini

Roger

2019

Journal of Chemometrics

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With the development of technology and the relatively higher availability of new instrumentations, having multiblock data sets (eg, a set of samples analyzed by different analytical techniques) is becoming more and more common and, as a consequence, how to handle this kind of outcomes is a widely discussed topic. In such a context, where the number of involved variables is relatively high, selecting the most significant features is obviously relevant.For this reason, the possibility of joining a multiblock regression method, the sequential and orthogonalized partial least-squares (SO-PLS), with a variable selection approach called covariance selection (CovSel), has been investigated. The resulting method, sequential and orthogonalized covariance selection (SO-CovSel) is similar to SO-PLS, but the feature reduction provided by PLS is performed by CovSel. Finally, predictions are made by applying multiple linear regression on the subset of selected variables. The novel approach has been tested on different multiblock data sets both in regression and in classification (by combination with LDA), and it has been compared with another state-ofthe-art multiblock method. SO-CovSel has demonstrated to be suitable for its purpose: It has provided good predictions (both in regression and in classification) and, from the interpretation point of view, it has led to a meaningful selection of the original variables. variables in this framework has not widely been discussed yet. In the present paper, the possibility of plugging together a multiblock latent variable-based method, sequential and orthogonalized partial least-squares (SO-PLS), 4 and a variable selection method, covariance selection (CovSel), 5 has been examined, and a novel multiblock variable selection method, named SO-CovSel, has been developed.Among the many possible methodologies which have been proposed in literature, CovSel and SO-PLS have been chosen because they have demonstrated to lead to good predictions and, at the same time, both of them present some peculiarities which make them particularly suitable for the interpretation of complex systems.Analogously to SO-PLSR and SO-PLS-LDA, 6 SO-CovSel finds its application in different contexts; it can be combined with regression or with linear discriminant analysis (LDA). 7 In both contexts, it has led to selections of variables exceptionally suitable for the interpretation of the systems under study, and it has provided predictions which are comparable with those obtained applying another state-of-the-art method. | MATERIAL AND METHODS | Covariance selectionCovSel 5 is a variable selection method conceived to detect the most relevant features in a regression context. As the name suggests, the relevancy of each variable is evaluated estimating the covariance between each predictor and responses. Once the variable presenting the highest covariance is individuated and selected, all the other predictors and the responses are orthogonalized with respect to it, and the procedure is repeated until the fixed amount of...

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