Karine Alary’s research while affiliated with University of Reunion Island and other places

Internal disorder is a major problem in fruit production and is responsible for considerable economical losses. Symptoms are not externally visible, making it difficult to assess the problem. In recent years, 3D fluorescence spectroscopy has been used to reveal features of interest in agronomical field, such as plant stress and plant infection. Such technique could provide useful information regarding changes that occur at the tissue level, in order to distinguish spectral differences between healthy and disordered fruits. This paper introduces the use of the new three-way feature extraction N-CovSel method, compared to the commonly used N-PLS-DA method. These approaches were used upon front-face fluorescence spectra of 27 fruit pulp and skin samples, by analysing excitation wavelengths ranging from 250 to 650 nm, and emission wavelengths varying from 290 to 800 nm. N-CovSel method was applied to identify the most relevant features on: 1) excitation-emission wavelength couples, 2) excitation wavelengths whatever the emission wavelengths and 3) emission wavelengths whatever the excitation wavelengths. Discriminant analysis of the selected features were performed across classes. The constructed models provided key features to differentiate healthy fruits from disordered ones. These results highlighted the capability of N-CovSel method to extract the most fitted features for enhanced fruit classification using front-face fluorescence spectroscopy. They revealed characteristic fluorophores involved in the structural modifications generated by the physiological disorder studied. This paper provides preliminary results concerning the suitability of N-CovSel method for the desired application. Further investigations could be performed on intact fresh fruits in a non-destructive way, allowing an earlier and faster detection of the internal disorder for in-field or industrial applications.

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.