Classification of cocoa beans based on their fluorescent fingerprint to predict sensory poles of chocolates?

Alary Karine, Preys Sébastien, Hue Clotilde, Descalzo Adriana Maria, Maraval Isabelle, Davrieux Fabrice, Boulanger Renaud. 2018. Classification of cocoa beans based on their fluorescent fingerprint to predict sensory poles of chocolates?. In : Cocotea 2019 book of abstracts. Jacobs University Bremen. Bremen : Jacobs University Bremen, Résumé, 57. International Congress on Cocoa Coffee and Tea. 5, Bremen, Allemagne, 26 Juin 2019/28 Juin 2019.

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Résumé : Natures and quantities of aroma compounds present in chocolate vary according to several criteria such as the origin and the variety of cocoa beans, the cocoa post-harvest treatment and the process of manufacturing chocolate. These organoleptic qualities are evaluated through sensory evaluation. This method enable to define the sensory profiles of chocolates and then their belonging to a sensory pole. Could a classification of merchantable cocoa beans based on their fluorescent fingerprint be an alternative to predict sensory poles of chocolate? The objective of our study was to develop a chemometric model obtain with fluorescent fingerprint. To do this, 3D spectral analyses were performed at 20°C by Front Face Fluorescence Spectroscopy (FFFS) on refined cocoa powder samples (N=208). All of them were analyzed following similar operating conditions. At the same time, a sensory analysis was performed on the corresponding dark chocolates, prepared by and standardized and controlled fabrication process. The prediction model was developed on the 208 samples divided into the four sensory poles, and validated by a set of 50 samples. The prediction error was around 30%. To interpret the data, preprocessing of signals and cleaning of non-informative areas (Rayleigh scattering) was carried out. Subsequently, a multiway exploratory analysis (PARAFAC) was carried out to determine the discriminant wavelengths in the distribution of classes. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were performed on spectral data to identify sensory pole separation and to elaborate chemometric model. As a result, analysis of fluorescent fingerprints enabled to reach a reliable distribution of cocoa beans according to the sensory pole of chocolate.

Auteurs et affiliations

• Alary Karine, CIRAD-PERSYST-UMR Qualisud (FRA)
• Preys Sébastien, ONDALYS (FRA)
• Hue Clotilde, Chocolaterie Valrhona (FRA)
• Descalzo Adriana Maria, CIRAD-PERSYST-UMR Qualisud (FRA)
• Maraval Isabelle, CIRAD-PERSYST-UMR Qualisud (FRA)
• Davrieux Fabrice, CIRAD-PERSYST-UMR Qualisud (REU)
• Boulanger Renaud, CIRAD-PERSYST-UMR Qualisud (FRA) ORCID: 0000-0001-9396-5634

Source : Cirad-Agritrop (https://agritrop.cirad.fr/593399/)

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