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cited By 11; International audience; In the present study, direct flow injection mass spectrometry was investigated for rapid characterization of the polyphenolic composition of red wines. Atmospheric pressure chemical ionization (APCI) and electrospray ionization (ESI) (in both positive and negative ion modes) have been simultaneously used for a more comprehensive analysis of the samples studied. In this way, four mass spectra have been recorded for each wine. Each spectrum was considered as a fingerprint related to the chemical composition. This methodology was applied to a large number of Beaujolais wines from different grades and different vintages.This data set was processed using a chemometrical multiblock analysis, which allowed to synthesize the whole information collected. The results obtained showed that the wine fingerprints address the composition of the main polyphenolic compounds present in the red wines and can discriminate groups of wines showing different polyphenolic compositions. Multiblock analysis appears as a very promising tool to deal with several data tables of multivariate signals in order to define, by combining the whole information, the best operating protocol according to the desired analytical objectives. © 2010 Elsevier B.V.

* 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.

cited By 39; International audience; Lower molecular weight polyphenols including proanthocyanidin oligomers can be analyzed after HPLC separation on either reversed-phase or normal phase columns. However, these techniques are time consuming and can have poor resolution as polymer chain length and structural diversity increase. The detection of higher molecular weight compounds, as well as the determination of molecular weight distributions, remain major challenges in polyphenol analysis. Approaches based on direct mass spectrometry (MS) analysis that are proposed to help overcome these problems are reviewed. Thus, direct flow injection electrospray ionization mass spectrometry analysis can be used to establish polyphenol fingerprints of complex extracts such as in wine. This technique enabled discrimination of samples on the basis of their phenolic (i.e. anthocyanin, phenolic acid and flavan-3-ol) compositions, but larger oligomers and polymers were poorly detectable. Detection of higher molecular weight proanthocyanidins was also restricted with matrix-assisted laser desorption ionization (MALDI) MS, suggesting that they are difficult to desorb as gas-phase ions. The mass distribution of polymeric fractions could, however, be determined by analyzing the mass distributions of bovine serum albumin/proanthocyanidin complexes using MALDI-TOF-MS. © 2008 Elsevier Ltd. All rights reserved.

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.

Biochemical methane potential (BMP) is essential to determine the production of methane for various substrates, literature shows important discrepancies for the same substrates. In this paper, a harmonized BMP protocol was developed and tested with two phases of BMP tests carried out by eleven French laboratories. Surprisingly, for the three same solid tested substrates (straw, raw mix and dried-shredded mix of potatoes, maize, beef meat and straw, and mayonnaise), the standard deviations of the repeatability and reproducibility inter-laboratory were not enhanced by the harmonized protocol (average of about 25% depending on the substrate), as compared to a previous step where all laboratories used their own protocols. Moreover, statistical analyses of all the results, after removal of the outliers (about 15% of all observations), did not highlight significant effect of the operational effect on BMP (stirring, automatic or manual gas quantification, use of trace metal, uses a bicarbonate buffer, inoculum to substrate ratio) at least for the tested ranges. On the other hand, the average intra-laboratory repeatability was low, about 7%, whatever the protocol, the substrate and the laboratory. It also appears that drying the SA substrate, which contained proteins, carbohydrates, lipids and fibers, does not impact its BMP.

International audience; Miniaturized near infrared spectrometers are now available, at more affordable prices than conventional spectrometers, but their performances have been poorly studied to date. This paper aimed at comparing the performances of the JDSU MicroNIR 2200 spectrophotometer (weight < 0.1 kg) with those of a conventional bench-top instrument for predicting carbon and nitrogen contents in laboratory conditions, on a range of representative Malagasy soils. Though its noticeably narrower and less resolved spectra (1151-2186 nm at 8.15 nm step vs. 1100-2498 nm at 2 nm step), the microspectrometer yielded predictions in independent validation that were almost as accurate as those of the conventional instrument (standard errors of prediction were 4.6 vs. 3.4 gC kg(-1), but 3.9 vs. 3.4 gC kg(-1) after bias correction, and 0.36 vs. 0.35 gN kg(-1), respectively). Due to noisy features, the MicroNIR spectra needed mathematical pretreatment (e.g. standard normal variate SNV), and bias correction for C, for providing accurate predictions, while the raw absorbance spectra from the conventional instrument did not. Furthermore, building multivariate models with MicroNIR spectra required less latent variables than with their conventional counterparts, and these models were less prone to performance degradation when applied to independent validation samples. Fitting the spectra of the conventional instrument to those of the MicroNIR (1150-2182 nm at 2 or 8 nm step) showed that (moderately) less accurate MicroNIR predictions could be firstly attributed to narrower spectral range rather than to poorer resolution. Considering their performances, such microspectrometers could thus represent a cost-effective alternative to conventional spectrometers. They have now to be tested in field conditions.

International audience; Direct-injection mass spectrometry (DIMS) techniques have evolved into powerful methods to analyse volatile organic compounds (VOCs) without the need of chromatographic separation. Combined to chemometrics, they have been used in many domains to solve sample categorization issues based on volatilome determination. In this paper, different DIMS methods that have largely outperformed conventional electronic noses (e-noses) in classification tasks are briefly reviewed, with an emphasis on food-related applications. A particular attention is paid to proton transfer reaction mass spectrometry (PTR-MS), and many results obtained using the powerful PTR-time of flight-MS (PTR-ToF-MS) instrument are reviewed. Data analysis and feature selection issues are also summarized and discussed. As a case study, a challenging problem of classification of dark chocolates that has been previously assessed by sensory evaluation in four distinct categories is presented. The VOC profiles of a set of 206 chocolate samples classified in the four sensory categories were analysed by PTR-ToF-MS. A supervised multivariate data analysis based on partial least squares regression-discriminant analysis allowed the construction of a classification model that showed excellent prediction capability: 97% of a test set of 62 samples were correctly predicted in the sensory categories. Tentative identification of ions aided characterisation of chocolate classes. Variable selection using dedicated methods pinpointed some volatile compounds important for the discrimination of the chocolates. Among them, the CovSel method was used for the first time on PTR-MS data resulting in a selection of 10 features that allowed a good prediction to be achieved. Finally, challenges and future needs in the field are discussed.

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

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.

NIR spectroscopic models lack robustness towards the influence of external parameters. Orthognosalisation techniques have demonstrated their ability to improve model robustness with regard to external parameter effects. The dynamic orthogonal projection (DOP)method is applied to the correction of the growing season effect on wheat protein content prediction using NIR spectroscopy. A few reference samples are measured at the beginning of each harvest. For each one,DOP generates an "ideal spectrum" by applying a reference-centred linear kernel on the calibration database. The parasitic sub-space, spanned by the differences between ideal and measured spectra, is then removed from the calibration space, by means of an orthogonal projection. The application database covered eight years of wheat NIR spectra related to protein content. The first four years were used for calibration and teh remaining years for testing. The corrections were calculated using the first scanned samples of every year, for each variety [between five and ten samples] (four samples per year). Results were compared with standard partial least suares calibration models.; Les modèles d'étalonnage de spectrométrie proche infrarouge souffrent d'un manque de robustesse vis à vis des grandeurs d'influence externes. Les techniques d'orthogonalisation ont démontré leur capacité à améliorer cette robustesse. La Projection Orthogonale Dynamique (DOP) est appliquée à la correction des effets de millésime pour la prédiction de la teneur en protéine du blé par spectrométrie NIR. Quelques échantillons de référence sont mesurés au début de chaque récolte et pour chacun d'eux, DOP génère un "spectre idéal", par application d'un noyau centré sur la valeur de référence, sur la base d'étalonnage. L'espace parasite, engendré par les différences observées entre spectres réels et idéaux, est ensuite enlevée par projection orthogonale. La base de données utilisée pour l'application couvrait 8 années de collectes de spectres de blé, en lien avec leur taux de protéine. Les 4 premières années ont servi à l'étalonnage, et les autres pour le test. Les corrections ont été calculées en utilisant les premiers échantillons de chaque année, pour chaque variété (enter 5 et 10 individus selon l'année). Les résultats sont comparés avec des modèles PLS standards.

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.