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International audience; Chocolate quality is largely due to the presence of polyphenols and especially of flavan-3-ols and their derivatives that contribute to bitterness and astringency. The aim of the present work was to assess the potential of a quantitative polyphenol targeted metabolomics analysis based on mass spectrometry for relating cocoa bean polyphenol composition corresponding chocolate polyphenol composition and sensory properties. One-hundred cocoa bean samples were transformed to chocolates using a standard process, and the latter were attributed to four different groups by sensory analysis. Polyphenols were analyzed by an ultra-high-performance liquid chromatography (UPLC) system hyphenated to a triple quadrupole mass spectrometer. A multiblock method called a Common Component and Specific Weights Analysis (CCSWA) was used to study relationships between the three datasets, i.e., cocoa polyphenols, chocolate polyphenols and sensory profiles. The CCSWA multiblock method coupling sensory and chocolate polyphenols differentiated the four sensory poles. It showed that polyphenolic and sensory data both contained information enabling the sensory poles' separation, even if they can be also complementary. A large amount of variance in the cocoa bean and corresponding chocolate polyphenols has been linked. The cocoa bean phenolic composition turned out to be a major factor in explaining the sensory pole separation.

* Methods based on near infrared spectroscopy used to assess wood properties are susceptible to variations in physical parameters (temperature, grain size, etc.). As wood is a hygroscopically sensitive material, we studied the effects of moisture on near infrared absorbance and calibration to accurately determine the application potential of this technique under routine. * A collection of Eucalyptus urophylla X E. grandis hybrid wood pieces were analysed to obtain reference calibration of polyphenol contents in wood extracts via NIR spectra acquired under constant moisture conditions. Other specimens from the same source were assessed to obtain spectra for eight moisture contents spanning a broad variation range. The effects of moisture on absorption and on estimates based on a reference model were analysed. * An increase in moisture content prompted a rise in near infrared absorption over the entire spectrum and for water O-H absorption bands. The polyphenol content estimates obtained by assessing specimens against the reference calibration at variable moisture contents revealed prediction bias. Five correction methods were then tested to enhance the robustness relative to moisture. * In-depth calibration and external parameter orthogonalization (EPO) were found to be the most efficient methods for offsetting this factor., * Les méthodes basées sur la spectroscopie proche infrarouge pour estimer les propriétés du bois sont sensibles aux variations des paramètres physiques (température, granulométrie...). Le bois étant un matériau hygroscopique sensible, l'influence de l'humidité sur l'absorbance et l'étalonnage proche infrarouge a été étudiée afin de mieux considérer les possibilités d'applications dans des conditions réelles. * Un étalonnage de référence de la quantité de polyphénols présents dans les extraits a été établi à partir de spectres d'une collection de bois d'hybrides d'Eucalyptus urophylla X E. grandis à humidité constante fixée. D'autres spectres ont été obtenus sur des échantillons de même provenance mais à 8 teneurs en eau couvrant une large plage de variation. L'influence de l'humidité sur l'absorbance proche infrarouge puis sur l'estimation par le modèle de référence a été analysée. * Une augmentation de l'humidité élève la ligne de base du spectre d'absorbance et également les régions d'absorbance caractéristiques de la liaison O-H de l'eau. Les estimations de la quantité de polyphénols à partir de l'étalonnage de référence sur les échantillons à humidité variable révèlent un biais sur les prédictions. * Parmi les cinq méthodes de correction testées pour rendre l'étalonnage robuste vis-à-vis de l'humidité, l'étalonnage exhaustif et l'EPO (External Parameter Orthogonalisation) s'avèrent être les plus efficaces et permettent de s'affranchir de ce facteur.

International audience; 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.

Un des enjeux majeurs de spectroscopie NIR est le transfert des méthodes développées en laboratoire vers le domaine industriel pour des applications en ligne ou portable. Il apparaît ainsi de nouvelles problématiques telles que la nécessité de robustesse ou, l'aptitude des modèles à prendre en compte les non-linéarités présentes dans les données. Les méthodes semi-paramétrique ont été suggérées comme outils pour compenser ce type de problèmes.Dans cet article, les régressions 'Least-Squared Support Vector Machine (LS-SVM)' sont mis en oeuvre pour la prédir l'acidité de trois différentes variétés de raisins à partir de leur spectre NIR. Les performances sont comparés au modèles utilisant des techniques classiques (PLSR, MLR). Les LS-SVM permettent s'obtenir de meilleures prédictions. Néanmoins un prétraitement par SNV est nécéssaire afin d'améliorer la robustesse des modèles / Nowadays, near infrared (NIR)technology is being transferred from the laboratory to the industrial world for on-line and portable applications. As a result, new issues are arising, such as the need for increased robustness, or the ability to compensate for non-linearities in the calibration or instrument. Semi-parametric modeling has been suggested as a means for adapting to these complications. In this article, Least-Squared Support Vector Machine (LS-SVM) regression, a semi-parametric modeling technique, is used to predict the acidity of three different grape varieties using NIR spectra. The performance and robustness of LS-SVM regression are compared to Partial Least Square Regression (PLSR) and Multivariate Linear Regression (MLR). LS-SVM regression produces more accurate prediction. However SNV pretreatment is required to improve the model robustness

Following up on the success of previous chemometric challenges arranged during the annual congress organised by the French Chemometrics Society, the organisation committee decided to repeat the idea for the Chimiometrie 2007 event (http://www.chimiometrie.org/) held in Lyon, France (2930 November) by featuring another dataset on its website. As for the first contest in 2004, this dataset was selected to test the ability of participants to apply regression methods to NIR data. The aim of Challenge 2007 was to perform a calibration model as robust and precise as possible using a data set with only a few reference values available and submitted to different perturbation factors. The committee received nine answers; this paper summarizes the best three approaches, as well as the approach proposed by the organisers.

Huit méthodes de transfert d'étalonnage basées sur l'élimination ses signaux orthogonaux ont été comparées dans l'objectif de transférer les modèles d'étalonnage de teneur en huile et en protéine de soja: Les méthodes sont: Dynamic orthogonal projection (DOP), transfer by orthogonal projection (TOP), error removal by orthogonal subtraction (EROS), orthogonal signal correction (OSC), orthogonal projection to latent structures (O-PLS) ainsi que des modifcations et extensions de ces méthodes . Toutes ont été comparées sur le transfert inter-marques ou intra-marques, à partir de spectres obtenus sur 2 Foss Infratecs et deux Bruins OmegAnalyzerGs. La méthode donnant les résultats les plus prometteurs est la méthode DOP modifiée par la matrice des différences, alors que les méthodes TOP et EROS modifiées n'ont pas apporté d'amélioration. / Eight calibration transfer methods based on the removal of orthogonal signal were compared for the standardization of whole soybean protein and oil models. Dynamic orthogonal projection (DOP), transfer by orthogonal projection (TOP), error removal by orthogonal subtraction (EROS), orthogonal signal correction (OSC), and orthogonal projections to latent structures (O-PLS) as well as the modification and extension of some of these methods were compared in the transfer of models in intra and inter-brand situations using two Foss Infratecs and two Bruins OmegAnalyzerGs. For each brand, a master was designated and its models transferred onto the second unit of its network and the two units of the second brand. Calibration models were transferable from brand to brand with similar or better precision than when each instrument was calibrated on its own calibration set (for Infratec 1229, the relative predictive determinant (RPD) increased in intra and inter-brand calibration transfer situations from 10.42 to 11.45 and 10.57, with DOP and EROS respectively). Performance of each method was different across parameters, instruments, and validation sets. DOP modifications on the determination of the difference matrix showed promising results while TOP and EROS extensions to include variability specifically present in certain crop years did not bring any beneficial effects.

By use of time-resolved spectroscopy it is possible to separate light scattering effects from chemical absorption effects in samples. In the study of propagation of short light pulses in turbid samples the reduced scattering coefficient and the absorption coefficient are usually obtained by fitting diffusion or Monte Carlo models to the measured data by use of numerical optimization techniques. In this study we propose a prediction model obtained with a semiparametric modeling technique: the least-squares support vector machines. The main advantage of this technique is that it uses theoretical time dispersion curves during the calibration step. Predictions can then be performed by use of data measured on different kinds of sample, such as apples.