A novel approach to discriminate the volatilome of Vitis vinifera berries by selected ion flow tube mass Spectrometry analysis and chemometrics - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Accéder directement au contenu
Article Dans Une Revue Food Research International Année : 2022

A novel approach to discriminate the volatilome of Vitis vinifera berries by selected ion flow tube mass Spectrometry analysis and chemometrics

Résumé

This study investigated a quick way to discriminate grape varieties based on their composition in volatile compounds through a SIFT-MS scan coupled with simple chemometrics approaches such as analysis of variance (ANOVA), principal component analysis (PCA) and hierarchical ascendant classification (HAC). The 23 studied grape varieties were distinguishable using O2+, H3O+ and NO+ as reagent ions, and the combination of these three ions. For its ability to ionize most compounds, to efficiently fragment them to generate ions with distinct m/z ratio, and to enhance the differentiation of compounds of similar masses, O2+ reagent ion should be preferentially considered. The use of one single ion rather than three enables to limit the time of analysis and the number of variables to be treated. The technique allowed the distinction of high and low aroma compounds producers as confirmed by headspace solid-phase microextraction followed by gas chromatography-mass spectrometry (HS-SPME/GC-MS) analyses. SIFT-MS is a quick and interesting tool with potential application in various fields of viticulture such as phenotyping of grape varieties or non-targeted studies on the impact of environmental factors or viticultural practices on grape aroma composition.

Dates et versions

hal-03709944 , version 1 (30-06-2022)

Identifiants

Citer

Thomas Baerenzung, Olivier Yobrégat, Alban Jacques, Valérie Simon, Olivier Geffroy. A novel approach to discriminate the volatilome of Vitis vinifera berries by selected ion flow tube mass Spectrometry analysis and chemometrics. Food Research International, 2022, 157, pp.111434. ⟨10.1016/j.foodres.2022.111434⟩. ⟨hal-03709944⟩
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