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.

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.

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.

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.

Following up on the success of previous chemometric challenges arranged during the annual congress organised by the French Chemome trics Society, the organisation committee decided to repeat the idea for the Chimio metrie 2007 event (http://www.chimiometrie.fr/ ) held in Lyon, France (29-30 November) by featurin g another dataset on its website. As for the first co ntest in 2004, this dataset was selected to test the ability of participants to app ly regression methods to NIR data. The aim of Challenge 2007 was to perform a calibrat ion model as robust and precise as possible with only a few reference values availa ble. The committee received nine answers; this paper summarizes the best three appro aches, as well as the approach proposed by the organisers.

A French inter-laboratory campaign on BMP assessment was realised in two phases: free protocol at first, then harmonized protocol. Results show good intra-laboratory repeatability and reproducibility, but in spite of an attempt in the harmonization of practices, the inter-laboratory reproducibility could not be improved and ranges in the magnitude of 20% RSD.

The implementation of a blend monitoring and control method based on a process analytical technology such as near infrared spectroscopy requires the selection and optimization of numerous criteria that will affect the monitoring outputs and expected blend end-point. Using a five component formulation, the present article contrasts the modeling strategies and end-point determination of a traditional quantitative method based on the prediction of the blend parameters employing partial least-squares regression with a qualitative strategy based on principal component analysis and Hotelling's T(2) and residual distance to the model, called Prototype. The possibility to monitor and control blend homogeneity with multivariate curve resolution was also assessed. The implementation of the above methods in the presence of designed experiments (with variation of the amount of active ingredient and excipients) and with normal operating condition samples (nominal concentrations of the active ingredient and excipients) was tested. The impact of criteria used to stop the blends (related to precision and/or accuracy) was assessed. Results demonstrated that while all methods showed similarities in their outputs, some approaches were preferred for decision making. The selectivity of regression based methods was also contrasted with the capacity of qualitative methods to determine the homogeneity of the entire formulation.

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 (29–30 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.

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.

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.

Many pre-processing methods aim at improving calibration model robustness in relation to the effect of an influence factor G. Orthogonal projection methods, such as OSC (Orthogonal Signal Correction) or EPO (External Parameter Orthogonalisation), are particularly well suited to process existing calibration databases. This work proposes a pre-processing strategy for the numerous cases where G variability is missing in the existing calibration database, and where effects of G and Y, the variable of interest, are not independent. The application in this study concerns the correction of the light scattering effect in NIR turbid spectra of grape musts. Ethanol content was thus correctly predicted (RMSEP=0.5 degrees) on very turbid samples (below 3000 NTU), much better than using all other geometric or multidimensional existing pre-processings tested. (C) 2007 Elsevier B.V. All rights reserved.

Authentication basically consists in deciding if a given unknown product belongs or not to a group of interest, defined by producers or regulators. More often, in order to demonstrate the authentication ability of a given instrumental analysis, several other groups are arbitrarily chosen. Then a Factorial or Linear Discriminant Analysis (FDA or LDA) or a Partial Least Squares Discriminant Analysis (PLS-DA) is usually performed; the model therefore depends on the nature of all observed groups of the study. The aim of this paper was to investigate an approach, named “prototype approach”, based on a model built up only using the group of products of interest. Such an approach has the advantage not to depend on the whole complementary data of the study.Prototype approach is inspired by Multivariate Statistical Process Control and Hotelling T2 statistic and consists in buiding up the assignment model according to the group of interest. Then, authentication step of new data is performed. Prototype approach and FDA were compared on a case study (authentication of Beaujolais red wines using their polyphenolic composition). False negative (#FN) and false positive (#FP) numbers were estimated by bootstrapping procedures for both methods.Compared to FDA, the prototype approach gave higher #FP with larger variability and lower #FN with lower variability. Wines produced with the same grape variety as AOC Beaujolais but in other regions were poorly authenticated. The prototype approach appears to be more flexible than FDA. The user can adjust the theoretical α risk in relation to its strategy, making that decision tool an alternative to discriminant analyses for authentication.

Polyphenolic compounds are responsible for important sensory properties of red wines (especially colour, astringency and, to a lesser extent, bitterness). Various model solution studies have investigated the relationships between specific phenolic compounds and sensory perception. The purpose of the present study was to relate polyphenolic composition to sensory data for a very large number of different commercial wines, using multiway analyses. Two homogeneous populations of commercial red wines (61 French and 60 German wines) were analyzed. Thirty simple polyphenolic compounds (anthocyanins, flavonols and phenolic acids) and some red pigment derivatives have been quantified using direct injection by liquid chromatography (LC) coupled to diode array detection and mass spectrometry. Condensed tannins were analyzed using LC after thiolysis. Sensory perception was assessed using descriptive analysis by four trained panels (2 countries×2 years). We first checked the consistency of the sensory data both through the 2 assessment years, and through the two countries. Although reproducibility was high, especially through countries, slight scale factor differences and scale shifts were detected. A chemical consensus was then built using common components and specific weights analysis for both wine subsets, where the sensory variables of each panel were projected separately. Although data structure was very different between the two wine subsets, common main chemical-sensory relationships were confirmed. The hypothesis of a relationship between flavonol aglycones and bitterness was raised. A partial least squares regression was performed and predicted linearly astringency with a R2 value of 0.80, but not colour intensity and hue, due to non-linear relationship and saturation of visual perception.

La nature et la quantite des composes d'arome presents dans le chocolat varient selon plusieurs criteres, comme la variete des feves de cacao, le processus de production du cacao marchand et le processus de fabrication du chocolat. Evaluees sensoriellement, ces qualites organoleptiques permettent de definir les profils sensoriels des chocolats et de les classer dans des poles sensoriels caracteristiques. Ces evaluations sont essentielles mais demeurent encore trop longues et onereuses. Une classification des chocolats a partir de leurs empreintes globales fluorescentes pourrait-elle etre une solution alternative pour identifier les poles sensoriels ? L'objectif de notre etude etait de differencier 200 chocolats noirs repartis suivant 4 poles sensoriels (A, B, C, D) a partir de leur " empreinte globale fluorescente " sans passer par une etape de degustation associee. Pour ce faire, des analyses spectrales 3D ont ete realisees par Spectrometrie de Fluorescence Frontale sur des echantillons de chocolats fondus a 45°C. Les chocolats ont tous ete prepares suivant le meme processus de fabrication, evalues sensoriellement puis analyses dans les memes conditions operatoires (LO excitation : 250-650 nm_ pas de 5nm ; LO emission 290-800nm_ pas de 2nm). Afin de clarifier les donnees, un pretraitement des signaux et une gestion des zones non informatives (diffusion Rayleigh) ont ete effectues. Par la suite, une analyse exploratoire multiway (PARAFAC) a ete realisee de maniere a connaitre l'importance des longueurs d'ondes dans la repartition des classes. Une Analyse en Composante Principale (ACP) a egalement ete realisee sur l'ensemble des donnees spectrales afin de voir si une separation des poles etait visible. Les analyses des empreintes globales fluorescentes ont ainsi permis d'en deduire une repartition fiable des chocolats suivant leur pole sensoriel sans passer par une etape de degustation. (Resume d'auteur)