Elucidating the Color of Rosé Wines Using Polyphenol-Targeted Metabolomics.

The color of rosé wines is extremely diverse and a key element in their marketing. It is due to the presence of anthocyanins and of additional pigments derived from them and from other wine constituents. To explore the pigment composition and determine its links with color, 268 commercial rosé wines were analysed. The concentration of 125 polyphenolic compounds was determined by a targeted metabolomics approach using ultra high-performance liquid chromatography coupled to triple quadrupole mass spectrometry (UHPLC-QqQ-MS) analysis in the Multiple Reaction Monitoring (MRM) mode and the color characterised by spectrophotometry and CieLab parameters. Chemometrics analysis of the composition and color data showed that although color intensity is primarily determined by polyphenol extraction (especially anthocyanins and flavanols) from the grapes, different color styles correspond to different pigment compositions. The salmon shade of light rosé wines is mostly due to pyranoanthocyanin pigments, resulting from reactions of anthocyanins with phenolic acids and pyruvic acid, a yeast metabolite. Redness of intermediate color wines is related to anthocyanins and carboxypoyranoanthocyanins and that of dark rosé wines to products of anthocyanin reactions with flavanols while yellowness of these wines is associated to oxidation.

High-resolution mass spectrometry (HRMS): Focus on the m/z values estimated by the Savitzky-Golay first derivative.

RATIONALE: The calculation of centroids from profile mass spectra is one of the very first steps in the processing of mass spectra. The output is a signal and a m/z value. We focus on the accuracy of the prediction of the centroids' m/z values. METHODS: A calculation based on the Savizky-Golay algorithm was evaluated on an Orbitrap mass spectrum. Reference m/z values were identified manually. Experimental centroids were extracted (1) automatically using our algorithm or the MSconvert algorithm and (2) manually using the Xcalibur software from Thermo. The three series of experimental m/z values were compared with the reference m/z values. RESULTS: Our algorithm improved the determination of the m/z values compared with MSconvert. However, no improvement was observed over Xcalibur. CONCLUSIONS: Our algorithm improved the automatic estimation of m/z values in the profile-to-centroid calculation. This is of importance when the goal is to determine raw compositions from the experimental m/z values. Nevertheless, the algorithms led to almost the same m/z values on a higher resolution mass spectrum.

Influence of Grape Berry Maturity on Juice and Base Wine Composition and Foaming Properties of Sparkling Wines from the Champagne Region.

In sparkling wine cool-climate regions like Champagne, it is sometimes necessary to pick the healthy grape clusters that have a relatively low maturity level to avoid the deleterious effects of Botrytis cinerea. In such conditions, we know that classical oenological parameters (sugars, pH, total acidity) may change but there is little information concerning the impact of grape berry maturity on wine proteins and foaming properties. Therefore, healthy grapes (Chardonnay and Pinot meunier) in 2015 and 2016 were picked at different maturity levels within the range of common industrial maturity for potential alcohol content 8⁻11% v/v in the Champagne region. Base wine protein content and foamability, and oenological parameters in grape juice and their corresponding base wines, were investigated. The results showed that base wine protein contents (analyzed by the Bradford method and by electrophoresis) and foamability were higher when the grapes were riper. The Pearson's correlation test found significant positive correlations (r = 0.890⁻0.997, p < 0.05) between Chardonnay grape berry maturity degree (MD) and base wine foamability in both vintages. Strong correlations between MD and most of the oenological parameters in grape juice and base wine were also found for the two cultivars. Under the premise of guaranteed grape health, delaying harvest date is an oenological decision capable of improving base wine protein content and foamability.

A Fast and Robust UHPLC-MRM-MS Method to Characterize and Quantify Grape Skin Tannins after Chemical Depolymerization.

A rapid, sensitive, and selective analysis method using ultra high performance liquid chromatography coupled with triple-quadrupole mass spectrometry (UHPLC-QqQ-MS) has been developed for the characterization and quantification of grape skin flavan-3-ols after acid-catalysed depolymerization in the presence of phloroglucinol (phloroglucinolysis). The compound detection being based on specific MS transitions in Multiple Reaction Monitoring (MRM) mode, this fast gradient robust method allows analysis of constitutive units of grape skin proanthocyanidins, including some present in trace amounts, in a single injection, with a throughput of 6 samples per hour. This method was applied to a set of 214 grape skin samples from 107 different red and white grape cultivars grown under two conditions in the vineyard, irrigated or non-irrigated. The results of triplicate analyses confirmed the robustness of the method, which was thus proven to be suitable for high-throughput and large-scale metabolomics studies. Moreover, these preliminary results suggest that analysis of tannin composition is relevant to investigate the genetic bases of grape response to drought.

Focus on the relationships between the cell wall composition in the extraction of anthocyanins and tannins from grape berries.

Concentrations of anthocyanins and tannins after extraction from berries in wines and from skin macerations in model solutions have been studied for two grape varieties, two maturation levels and two vintages berries. Characterization of the cell wall polysaccharides has also been performed, the classical method based on the analysis of the neutral sugars after depolymerization being completed by a comprehensive microarray polymer profiling (CoMPP). Extraction was lower in model solutions than in wines, with the same ranking: non acylated anthocyanins> tannins > p-coumaroylated anthocyanins. The polysaccharidic composition suggested a role of homogalacturonans, rhamnogalacturonans and extensins in the extraction process. A global explanation of the interactions between anthocyanins, tannins and polysaccharides is proposed.

Shades of Fine Dark Chocolate Colors: Polyphenol Metabolomics and Molecular Networking to Enlighten the Brown from the Black.

High-quality dark chocolates (70% cocoa content) can have shades from light to dark brown color. This work aimed at revealing compounds that discriminate black and brown chocolates. From 37 fine chocolate samples from years 2019 and 2020 provided by Valrhona,8 dark black samples and 8 light brown samples were selected. A non-targeted metabolomics study was performed based on ultra-high performance liquid chromatography-high resolution mass spectrometry/mass spectrometry experiments, univariate, multivariate, and feature-based molecular networking analyses. Twenty-seven overaccumulated discriminating compounds were found for black chocolates. Among them, glycosylated flavanols including monomers and glycosylated A-type procyanidin dimers and trimers were highly representative. Fifty overaccumulated discriminating compounds were found for brown chocolates. Most of them were B-type procyanidins (from trimers to nonamers). These phenolic compounds may be partially related to the chocolate colors as precursors of colored compounds. This study increases the knowledge on the chemical diversity of dark chocolates by providing new information about the phenolic profiles of black and brown chocolates.

Study of the relationship between red wine colloidal fraction and astringency by asymmetrical flow field-flow fractionation coupled with multi-detection.

Macromolecules including condensed tannins and polysaccharides impact wine taste and especially astringency. Asymmetrical Flow-Field-Flow-Fractionation (AF4) coupled to UV detection (UV), multi-angle light scattering (MALS) and refractive index detection (dRI) has been proposed to separate red wine colloids. The present work aimed at relating AF4-mutidetection profiles with red wine astringency. Fifty commercial red wines characterized by a trained sensory panel were analysed by AF4-UV-MALS-dRI and UV-visible spectroscopy. The analytical data set was built by selecting the three variables most predictive of the astringency score from each table (UV, dRI, MALS, Mw distribution, and UV-visible spectra of whole wine, permeate and retentate A4F fractions) and analysed by principal component analysis. Red wine astringency was more related to variables extracted from the AF4 data than to UV- absorbance of the wine or permeate, confirming the relevance of AF4-multidetection for analysis of the colloidal fraction involved in this perception.

Fast Discrimination of Chocolate Quality Based on Average-Mass-Spectra Fingerprints of Cocoa Polyphenols.

This work aims to sort cocoa beans according to chocolate sensory quality and phenolic composition. Prior to the study, cocoa samples were processed into chocolate in a standard manner, and then the chocolate was characterized by sensory analysis, allowing sorting of the samples into four sensory groups. Two objectives were set: first to use average mass spectra as quick cocoa-polyphenol-extract fingerprints and second to use those fingerprints and chemometrics to select the molecules that discriminate chocolate sensory groups. Sixteen cocoa polyphenol extracts were analyzed by liquid chromatography-low-resolution mass spectrometry. Averaging each mass spectrum provided polyphenolic fingerprints, which were combined into a matrix and processed with chemometrics to select the most meaningful molecules for discrimination of the chocolate sensory groups. Forty-four additional cocoa samples were used to validate the previous results. The fingerprinting method proved to be quick and efficient, and the chemometrics highlighted 29 m/ z signals of known and unknown molecules, mainly flavan-3-ols, enabling sensory-group discrimination.

Fast characterization of solid organic waste content with near infrared spectroscopy in anaerobic digestion.

The development of anaerobic digestion involves both co-digestion of solid wastes and optimization of the feeding recipe. Within this context, substrate characterisation is an essential issue. Although it is widely used, the biochemical methane potential is not sufficient to optimize the operation of anaerobic digestion plants. Indeed the biochemical composition in carbohydrates, lipids, proteins and the chemical oxygen demand of the inputs are key parameters for the optimisation of process performances. Here we used near infrared spectroscopy as a robust and less-time consuming tool to predict the solid waste content in carbohydrates, lipids and nitrogen, and the chemical oxygen demand. We built a Partial Least Square regression model with 295 samples and validated it with an independent set of 46 samples across a wide range of solid wastes found in anaerobic digestion units. The standard errors of cross-validation were 90mgO2⋅gTS-1 carbohydrates, 2.5∗10-2g⋅gTS-1 lipids, 7.2∗10-3g⋅gTS-1 nitrogen and 99mgO2⋅gTS-1 chemical oxygen demand. The standard errors of prediction were 53mgO2⋅gTS-1 carbohydrates, 3.2∗10-2g⋅gTS-1 lipids, 8.6∗10-3g⋅gTS-1 nitrogen and 83mgO2⋅gTS-1 chemical oxygen demand. These results show that near infrared spectroscopy is a new fast and cost-efficient way to characterize solid wastes content and improve their anaerobic digestion monitoring.

Cultivar Diversity of Grape Skin Polyphenol Composition and Changes in Response to Drought Investigated by LC-MS Based Metabolomics.

Phenolic compounds represent a large family of plant secondary metabolites, essential for the quality of grape and wine and playing a major role in plant defense against biotic and abiotic stresses. Phenolic composition is genetically driven and greatly affected by environmental factors, including water stress. A major challenge for breeding of grapevine cultivars adapted to climate change and with high potential for wine-making is to dissect the complex plant metabolic response involved in adaptation mechanisms. A targeted metabolomics approach based on ultra high-performance liquid chromatography coupled to triple quadrupole mass spectrometry (UHPLC-QqQ-MS) analysis in the Multiple Reaction Monitoring (MRM) mode has been developed for high throughput profiling of the phenolic composition of grape skins. This method enables rapid, selective, and sensitive quantification of 96 phenolic compounds (anthocyanins, phenolic acids, stilbenoids, flavonols, dihydroflavonols, flavan-3-ol monomers, and oligomers…), and of the constitutive units of proanthocyanidins (i.e., condensed tannins), giving access to detailed polyphenol composition. It was applied on the skins of mature grape berries from a core-collection of 279 Vitis vinifera cultivars grown with or without watering to assess the genetic variation for polyphenol composition and its modulation by irrigation, in two successive vintages (2014-2015). Distribution of berry weights and δ13C values showed that non irrigated vines were subjected to a marked water stress in 2014 and to a very limited one in 2015. Metabolomics analysis of the polyphenol composition and chemometrics analysis of this data demonstrated an influence of water stress on the biosynthesis of different polyphenol classes and cultivar differences in metabolic response to water deficit. Correlation networks gave insight on the relationships between the different polyphenol metabolites and related biosynthetic pathways. They also established patterns of polyphenol response to drought, with different molecular families affected either positively or negatively in the different cultivars, with potential impact on grape and wine quality.

Models based on ultraviolet spectroscopy, polyphenols, oligosaccharides and polysaccharides for prediction of wine astringency.

Astringency elicited by tannins is usually assessed by tasting. Alternative methods involving tannin precipitation have been proposed, but they remain time-consuming. Our goal was to propose a faster method and investigate the links between wine composition and astringency. Red wines covering a wide range of astringency intensities, assessed by sensory analysis, were selected. Prediction models based on multiple linear regression (MLR) were built using UV spectrophotometry (190-400 nm) and chemical analysis (enological analysis, polyphenols, oligosaccharides and polysaccharides). Astringency intensity was strongly correlated (R(2) = 0.825) with tannin precipitation by bovine serum albumin (BSA). Wine absorbances at 230 nm (A230) proved more suitable for astringency prediction (R(2) = 0.705) than A280 (R(2) = 0.56) or tannin concentration estimated by phloroglucinolysis (R(2) = 0.59). Three variable models built with A230, oligosaccharides and polysaccharides presented high R(2) and low errors of cross-validation. These models confirmed that polysaccharides decrease astringency perception and indicated a positive relationship between oligosaccharides and astringency.

Application of direct calibration in multivariate image analysis of heterogeneous materials.

Many scientific instruments produce multivariate images characterized by three-way tables, an element of which represents the intensity value at a spatial location for a given spectral channel. A problem frequently encountered is to attempt estimating the contributions of some compounds at each location of these images. Usual regression methods of calibration, such as PLS, require having a matrix of calibration X (n×p) and the corresponding vector y of the dependent variable (n×1). X can be built up by sampling pixel-vectors in the images, but y is sometimes difficult to obtain, if the surface of the samples is formed by chemically heterogeneous regions. In this case, the quantitative analyses related to y may be difficult, if the pixels represent very small areas (for example on microscopic images) or very large ones (satellite images). This is for example the case when dealing with biological solid samples representing different tissues. Direct Calibration (DC), sometimes referred to as "spectral unmixing", do not require having such a calibration set. However, it is indeed needed to have both a matrix of "perturbing" pixel-vectors (noted K) and a vector of the "pure" component spectrum to be analyzed (p), which are more easily obtainable. For estimating the contribution, the unknown pixel vector x and the pure spectrum p are first projected orthogonally onto K giving the vectors x(⊥) onto p(⊥), respectively. The contribution is then estimated by a second projection of x(⊥) onto p(⊥). A method, based on principal component analysis, for determining the optimal dimensions of K is proposed. DC was applied on a collection of multivariate images of kernel of wheat to estimate the proportion of three tissues, namely out-layers, "waxy"endosperm and normal endosperm. The eventual results are presented as images of wheat kernels in false colors associated to the estimated proportions of the tissues. It is shown that DC is appropriate for estimating contributions in situations in which the more usual methods of calibration cannot be applied.

Improvement of direct calibration in spectroscopy.

Several linear calibration methods have been proposed for predicting the concentration of a particular compound from a spectrum. Some methods are based on experimental data, such as Partial Least Square Regression. Other methods are based on expert data, e.g. Direct Calibration. This article proposes a new method, called Improved Direct Calibration, which uses expert and experimental information. It performs a projection onto the pure interest spectrum, after correcting it from influence factors. No calibration dataset is necessary to build this model. This method has been successfully applied to the quantification of ethanol in musts during fermentation, using near infrared spectrometry.