Volatile compounds profiling by using proton transfer reaction-time of flight-mass spectrometry (PTR-ToF-MS). The case study of dark chocolates organoleptic differences
Zoé Deuscher
(1, 2)
,
Isabelle Andriot
(1, 2)
,
Etienne Sémon
(1, 2)
,
Marie Repoux
(3)
,
Sébastien Preys
(4)
,
Jean-Michel Roger
(5)
,
Renaud Boulanger
(6)
,
Hélène Labouré
(1, 2)
,
Jean-Luc Le Quéré
(1, 2)
Jean-Michel Roger
- Fonction : Auteur
- PersonId : 756502
- IdHAL : jean-michel-roger
- ORCID : 0000-0003-2123-5266
- IdRef : 112046711
Renaud Boulanger
- Fonction : Auteur
- PersonId : 1078118
- ORCID : 0000-0001-9396-5634
- IdRef : 164988041
Hélène Labouré
- Fonction : Auteur
- PersonId : 175581
- IdHAL : helene-laboure
- ORCID : 0000-0002-8173-6145
- IdRef : 116007613
Jean-Luc Le Quéré
Connectez-vous pour contacter l'auteur
- Fonction : Auteur correspondant
- PersonId : 1207428
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Résumé
Direct-injection mass spectrometry (DIMS) techniques have evolved into powerful methods to analyse volatile organic compounds (VOCs) without the need of chromatographic separation. Combined to chemometrics, they have been used in many domains to solve sample categorization issues based on volatilome determination. In this paper, different DIMS methods that have largely outperformed conventional electronic noses (e-noses) in classification tasks are briefly reviewed, with an emphasis on food-related applications. A particular attention is paid to proton transfer reaction mass spectrometry (PTR-MS), and many results obtained using the powerful PTR-time of flight-MS (PTR-ToF-MS) instrument are reviewed. Data analysis and feature selection issues are also summarized and discussed. As a case study, a challenging problem of classification of dark chocolates that has been previously assessed by sensory evaluation in four distinct categories is presented. The VOC profiles of a set of 206 chocolate samples classified in the four sensory categories were analysed by PTR-ToF-MS. A supervised multivariate data analysis based on partial least squares regression-discriminant analysis allowed the construction of a classification model that showed excellent prediction capability: 97% of a test set of 62 samples were correctly predicted in the sensory categories. Tentative identification of ions aided characterisation of chocolate classes. Variable selection using dedicated methods pinpointed some volatile compounds important for the discrimination of the chocolates. Among them, the CovSel method was used for the first time on PTR-MS data resulting in a selection of 10 features that allowed a good prediction to be achieved. Finally, challenges and future needs in the field are discussed.
Domaines
Sciences de l'environnementFormat du dépôt | Notice |
---|---|
Type de dépôt | Article dans une revue |
Résumé |
en
Direct-injection mass spectrometry (DIMS) techniques have evolved into powerful methods to analyse volatile organic compounds (VOCs) without the need of chromatographic separation. Combined to chemometrics, they have been used in many domains to solve sample categorization issues based on volatilome determination. In this paper, different DIMS methods that have largely outperformed conventional electronic noses (e-noses) in classification tasks are briefly reviewed, with an emphasis on food-related applications. A particular attention is paid to proton transfer reaction mass spectrometry (PTR-MS), and many results obtained using the powerful PTR-time of flight-MS (PTR-ToF-MS) instrument are reviewed. Data analysis and feature selection issues are also summarized and discussed. As a case study, a challenging problem of classification of dark chocolates that has been previously assessed by sensory evaluation in four distinct categories is presented. The VOC profiles of a set of 206 chocolate samples classified in the four sensory categories were analysed by PTR-ToF-MS. A supervised multivariate data analysis based on partial least squares regression-discriminant analysis allowed the construction of a classification model that showed excellent prediction capability: 97% of a test set of 62 samples were correctly predicted in the sensory categories. Tentative identification of ions aided characterisation of chocolate classes. Variable selection using dedicated methods pinpointed some volatile compounds important for the discrimination of the chocolates. Among them, the CovSel method was used for the first time on PTR-MS data resulting in a selection of 10 features that allowed a good prediction to be achieved. Finally, challenges and future needs in the field are discussed.
|
Titre |
en
Volatile compounds profiling by using proton transfer reaction-time of flight-mass spectrometry (PTR-ToF-MS). The case study of dark chocolates organoleptic differences
|
Auteur(s) |
Zoé Deuscher
1, 2
, Isabelle Andriot
1, 2
, Etienne Sémon
1, 2
, Marie Repoux
3
, Sébastien Preys
4
, Jean-Michel Roger
5
, Renaud Boulanger
6
, Hélène Labouré
1, 2
, Jean-Luc Le Quéré
1, 2
1
INRA -
Institut National de la Recherche Agronomique
( 92114 )
- France
2
UBFC -
Université Bourgogne Franche-Comté [COMUE]
( 426438 )
- 32, avenue de l’observatoire
25000 BESANCON
- France
3
Valrhona SAS
( 559303 )
- Tain L'Hermitage
- France
4
Ondalys
( 325652 )
- Clapiers
- France
5
UMR ITAP -
Information – Technologies – Analyse Environnementale – Procédés Agricoles
( 16792 )
- 361, rue J.F. Breton - BP 5095 - 34033 Montpellier Cedex 1
- France
6
UMR Qualisud -
Démarche intégrée pour l'obtention d'aliments de qualité
( 31878 )
- TA B95/16 - 73 rue Jean-François Breton 34398 Montpellier Cedex 5, France
- France
|
Localisation géographique du document |
UMR 1324 CSGA, INRA DIJON
|
URL éditeur |
http://wileyonlinelibrary.com/journal/jms
|
Sous-type de document pour les Articles |
Research article
|
Audience |
Internationale
|
Date de publication |
2019-01
|
Date de publication électronique |
2018-11-26
|
Volume |
54
|
Page/Identifiant |
92-119
|
Langue du document |
Anglais
|
Public visé |
Scientifique
|
Vulgarisation |
Non
|
Comité de lecture |
Oui
|
Nom de la revue |
|
Numéro |
1
|
Commentaire |
ProdInra 458058:
[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]INSPIRE [ADD1_IRSTEA]Équiper l'agriculture
|
Données associées | |
Domaine(s) |
|
Voir aussi |
|
Indexation contrôlée |
|
Commentaire(s) |
|
Financement |
|
Mots-clés |
en
CHOCOLATE, COVSEL, PROFILING, PTR-TOF-MS
fr
PLS-DA, VOCS
|
DOI | 10.1002/jms.4317 |
irste@doc (Irstea) | PUB00060423 |
ProdINRA | 454707 |
Pubmed Id | 30478865 |
UT key WOS | 000456555100012 |
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