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Key aroma compounds of dark chocolates differing in organoleptic properties: A GC-O comparative study

Deuscher Zoé, Gourrat Karine, Repoux Marie, Boulanger Renaud, Labouré Hélène, Le Quéré Jean-Luc. 2020. Key aroma compounds of dark chocolates differing in organoleptic properties: A GC-O comparative study. Molecules, 25 (8):1809, 33 p.

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Abstract : 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 (Khi² 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.

Mots-clés Agrovoc : Chocolat, Propriété organoleptique, Analyse organoleptique, Composé de la flaveur, Composé volatil, Méthode statistique

Mots-clés libres : Dark chocolate, Aroma, Heatmap, Key odorant, Gas chromatography-olfactometry (GC-O), Comparative detection frequency analysis (cDFA), Nasal impact frequency (NIF), Hierarchical cluster analysis (HCA), Correspondence analysis (CA)

Classification Agris : Q01 - Food science and technology - General aspects
Q54 - Feed composition
U30 - Research methods

Champ stratégique Cirad : CTS 3 (2019-) - Systèmes alimentaires

Auteurs et affiliations

  • Deuscher Zoé, AgroSup Dijon (FRA)
  • Gourrat Karine, AgroSup Dijon (FRA)
  • Repoux Marie, Chocolaterie Valrhona (FRA)
  • Boulanger Renaud, CIRAD-PERSYST-UMR Qualisud (FRA) ORCID: 0000-0001-9396-5634
  • Labouré Hélène, CNRS (FRA)
  • Le Quéré Jean-Luc, CNRS (FRA) - auteur correspondant

Source : Cirad-Agritrop (https://agritrop.cirad.fr/595524/)

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