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Article Dans Une Revue Journal of Agricultural and Food Chemistry Année : 2019

Fast Discrimination of Chocolate Quality Based on Average-Mass-Spectra Fingerprints of Cocoa Polyphenols

Noémie Fayeulle
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Emmanuelle Meudec
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Jean Claude Boulet
Clotilde Hue
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Veronique Cheynier
Nicolas Sommerer

Résumé

This work aims to sort cocoa beans according to chocolate sensory quality and phenolic composition. Prior tothe study, cocoa samples were processed into chocolate in a standard manner, and then the chocolate was characterized bysensory analysis, allowing sorting of the samples into four sensory groups. Two objectives were set:first to use average massspectra as quick cocoa-polyphenol-extractfingerprints and second to use thosefingerprints and chemometrics to select themolecules that discriminate chocolate sensory groups. Sixteen cocoa polyphenol extracts were analyzed by liquidchromatography−low-resolution mass spectrometry. Averaging each mass spectrum provided polyphenolicfingerprints,which were combined into a matrix and processed with chemometrics to select the most meaningful molecules fordiscrimination of the chocolate sensory groups. Forty-four additional cocoa samples were used to validate the previous results.Thefingerprinting method proved to be quick and efficient, and the chemometrics highlighted 29m/zsignals of known andunknown molecules, mainlyflavan-3-ols, enabling sensory-group discrimination.
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hal-02121864 , version 1 (06-05-2019)

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Noémie Fayeulle, Emmanuelle Meudec, Jean Claude Boulet, Anna Vallverdu Queralt, Clotilde Hue, et al.. Fast Discrimination of Chocolate Quality Based on Average-Mass-Spectra Fingerprints of Cocoa Polyphenols. Journal of Agricultural and Food Chemistry, 2019, 67 (9), pp.2723-2731. ⟨10.1021/acs.jafc.8b06456⟩. ⟨hal-02121864⟩
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