Multi-block classification of chocolate and cocoa samples into sensory poles. (15th March 2021)

• Record Type: Journal Article
• Authors: Biancolillo, Alessandra; Preys, Sebastien; Gaci, Belal; Le-Quere, Jean-Luc; Laboure, Helene; Deuscher, Zoe; Cheynier, Veronique; Sommerer, Nicolas; Fayeulle, Noemie; Costet, Pierre; Hue, Clotilde; Boulanger, Renaud; Alary, Karine; Lebrun, Marc; Christine Lahon, Marie; Morel, Gilles; Maraval, Isabelle; Davrieux, Fabrice; Roger, Jean-Michel
• Is Part Of: Food chemistry. Volume 340(2021)
• Publication Date: 2021-03-15
• Subjects: Chocolate -- Cocoa beans -- Multi-block -- Spectroscopy -- Chromatography -- Classification -- Sensory analysis; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2020.127904
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: 97 chocolate and cocoa beans samples were analysed by chemical and sensory analysis. Chemical data:HPLC, NIRS, Front face Fluorescence, SPME-GC-MS, PTR-MS, UHPLC-QqQ-MS. Samples were classified into four sensory poles by chemometric classifiers. Best results: SO-PLS-LDA on chocolate (2 misclassified samples over 31 of the test set) VIP analysis was used to inspect the most relevant variables. Abstract: The present study aims at developing an analytical methodology which allows correlating sensory poles of chocolate to their chemical characteristics and, eventually, to those of the cocoa beans used for its preparation. Trained panelists investigated several samples of chocolate, and they divided them into four sensorial poles (characterized by 36 different descriptors) attributable to chocolate flavor. The same samples were analyzed by six different techniques: Proton Transfer Reaction-Time of Flight-Mass Spectrometry (PTR-ToF-MS), Solid Phase Micro Extraction-Gas Chromatography-Mass Spectroscopy (SPME-GC-MS), High-Performance Liquid Chromatography (HPLC) (for the quantification of eight organic acids), Ultra High Performance Liquid Chromatography coupled to triple-quadrupole Mass Spectrometry (UHPLC-QqQ-MS) for polyphenol quantification, 3D front face fluorescence Spectroscopy and Near Infrared Spectroscopy (NIRS). A multi-block classification approach (Sequential and Orthogonalized-Partial Least Squares – SO-PLS) has been used, in order to exploit the chemical information to predict the sensorial poles of samples. Among thirty-one test samples, only two were misclassified.