Wine science in the metabolomics era

1H NMR and LC-MS-based metabolomic approach for evaluation of the seasonality and viticultural practices in wines from São Francisco River Valley, a Brazilian semi-arid region

São Francisco River Valley (SFRV) is a wine-producing semi-arid region in Brazil. Therefore, we used a ¹H NMR and UPLC-MS-based metabolomic approach coupled to chemometrics to evaluate the variability in Chenin Blanc and Syrah wines for two harvest seasons, two vine training system and six rootstocks. Overall, the secondary metabolites were influenced by the three factors studied, whereas the primary metabolites were only by the seasonality. Chenin Blanc wines made in December presented higher content of an unidentified carbohydrate. In Syrah wines, glycerol, tartaric acid, succinic acid and 2,3-butanediol were greater in December, while proline and lactic acid were more abundant in July. For training system, caffeic acid derivatives were increased in wines produced from espalier. Lyre system increased phenolic compounds, organic acids and apocarotenoids. The effect of the rootstocks was less pronounced, affecting basically caffeic acid derivatives. Thus, we expect that our results may assist the winemakers to improve the SFRV wine quality.

Quality and Authenticity Control of Fruit Juices-A Review

Food fraud, being the act of intentional adulteration of food for financial advantage, has vexed the consumers and the food industry throughout history. According to the European Committee on the Environment, Public Health and Food Safety, fruit juices are included in the top 10 food products that are most at risk of food fraud. Therefore, reliable, efficient, sensitive and cost-effective analytical methodologies need to be developed continuously to guarantee fruit juice quality and safety. This review covers the latest advances in the past ten years concerning the targeted and non-targeted methodologies that have been developed to assure fruit juice authenticity and to preclude adulteration. Emphasis is placed on the use of hyphenated techniques and on the constantly-growing role of MS-based metabolomics in fruit juice quality control area.

Wine and grape marc spirits metabolomics

INTRODUCTION

Mass spectrometry (MS)-based and nuclear magnetic resonance (NMR) spectroscopic analyses play a key role in the field of metabolomics due to their important advantages. The use of metabolomics in wine and grape marc spirits allows a more holistic perspective in monitoring and gaining information on the making processes and thus it can assist on the improvement of their quality.

OBJECTIVES

This review surveys the latest metabolomics approaches for wine and grape marc spirits with a focus on the description of MS-based and NMR spectroscopic analytical techniques.

METHODS

We reviewed the literature to identify metabolomic studies of wine and grape marc spirits that were published until the end of 2017, with the key term combinations of 'metabolomics', 'wine' and 'grape marc spirits'. Through the reference lists from these studies, additional articles were identified.

RESULTS

The results of this review showed that the application of different metabolomics approaches has significantly increased the knowledge of wine metabolome and grape marc spirits; however there is not yet a single analytical platform that can completely separate, detect and identify all metabolites in one analysis.

CONCLUSIONS

The authentication and quality control of wines and grape marc spirits has to be taken with caution, since the product's chemical composition could be affected by many factors. Despite intrinsic limitations, NMR spectroscopy and MS based strategies remain the key analytical methods in metabolomics studies. Authenticity, traceability and health issues related to their consumption are the major research initiatives in wine and grape marc spirits metabolomics analysis.

Rapid detection of three quality parameters and classification of wine based on Vis-NIR spectroscopy with wavelength selection by ACO and CARS algorithms

The feasibility of rapid detection of three quality parameters and classification of wines based on visible and near infrared spectroscopy (Vis-NIRs) was investigated. A modified ant colony optimization (ACO) algorithm for wavelength selection in Vis-NIR spectral analysis was proposed to improve the prediction performance of partial least squares regression (PLSR) model. The result proved that feature wavelengths/variables can be selected by the proposed method for building a high performance PLSR model. The root mean square error of total acid, total sugar and alcohol obtained by ACO-PLS were 0.00122 mol/l, 0.0893 g/l and 0.206 (v/v), respectively. Their correlation coefficients obtained by ACO-PLS reach to 0.973, 0.994 and 0.928, respectively. Compared with full-spectral PLS and PLS based on competitive adaptive reweighted sampling (CARS-PLS) method, the application of ACO wavelength selection provided a notably improved regression model. The prediction results were significantly better than the full-spectral PLS model and slightly better than the CARS-PLS method. Meanwhile, a classification study was also constructed based on the ACO-Principal component analysis (ACO-PCA) model showed that Vis-NIR spectra could be used to classify wines according to the geographical origins. Therefore, it can be concluded that the Vis-NIR spectroscopy technique based on ACO wavelength selection has high potential to differentiate the wine origins and predict the quality parameters in a nondestructive way.

Grape and Wine Metabolomics to Develop New Insights Using Untargeted and Targeted Approaches

Chemical analysis of grape juice and wine has been performed for over 50 years in a targeted manner to determine a limited number of compounds using Gas Chromatography, Mass-Spectrometry (GC-MS) and High Pressure Liquid Chromatography (HPLC). Therefore, it only allowed the determination of metabolites that are present in high concentration, including major sugars, amino acids and some important carboxylic acids. Thus, the roles of many significant but less concentrated metabolites during wine making process are still not known. This is where metabolomics shows its enormous potential, mainly because of its capability in analyzing over 1000 metabolites in a single run due to the recent advancements of high resolution and sensitive analytical instruments. Metabolomics has predominantly been adopted by many wine scientists as a hypothesis-generating tool in an unbiased and non-targeted way to address various issues, including characterization of geographical origin (terroir) and wine yeast metabolic traits, determination of biomarkers for aroma compounds, and the monitoring of growth developments of grape vines and grapes. The aim of this review is to explore the published literature that made use of both targeted and untargeted metabolomics to study grapes and wines and also the fermentation process. In addition, insights are also provided into many other possible avenues where metabolomics shows tremendous potential as a question-driven approach in grape and wine research.

Wine evolution during bottle aging, studied by 1H NMR spectroscopy and multivariate statistical analysis

The study of wine evolution during bottle aging is an important aspect of wine quality. Ten different red wines (Vitis vinifera) from Piedmont region were analysed 3 months after bottling and after a further 48 month conservation in a climate controlled wine cellar kept at a constant/controlled temperature of 12 °C. Two white wines (Vitis vinifera) were included in this study for comparison purposes. White wines were analysed 3 months after bottling and after further 24 months of bottle aging in the same climate controlled wine cellar. Metabolite changes during this period were evaluated using ¹H NMR spectroscopy combined with statistical analysis. Metabolite variations due to wine aging were minimal compared to those that resulted from a different wine type and wine geographical origin. Therefore, it was necessary to remove this source of variability to discriminate between fresh and refined samples. The storage at low and controlled temperature for 2 or 4 years permitted a slow but progressive evolution of all wines under investigation. 1H NMR spectroscopy, implemented with statistical data analysis, allowed identifying and differentiating wine samples from the two aging stages. In most wines, a decrease in organic acids (lactic acid, succinic acid and tartaric acid) and an increase in esters (ethyl acetate and ethyl lactate) was observed. Catechin and epicatechin decreased during aging in all wines while gallic acid increased in almost all red wines.

Field-Based Metabolomics of Vitis vinifera L. Stems Provides New Insights for Genotype Discrimination and Polyphenol Metabolism Structuring

Grape accumulates numerous polyphenols with abundant health benefit and organoleptic properties that in planta act as key components of the plant defense system against diseases. Considerable advances have been made in the chemical characterization of wine metabolites particularly volatile and polyphenolic compounds. However, the metabotyping (metabolite-phenotype characterization) of grape varieties, from polyphenolic-rich vineyard by-product is unprecedented. As this composition might result from the complex interaction between genotype, environment and viticultural practices, a field experiment was setting up with uniform pedo-climatic factors and viticultural practices of growing vines to favor the genetic determinism of polyphenol expression. As a result, UPLC-MS-based targeted metabolomic analyses of grape stems from 8 Vitis vinifera L. cultivars allowed the determination of 42 polyphenols related to phenolic acids, flavonoids, procyanidins, and stilbenoids as resveratrol oligomers (degree of oligomerization 1-4). Using a partial least-square discriminant analysis approach, grape stem chemical profiles were discriminated according to their genotypic origin showing that polyphenol profile express a varietal signature. Furthermore, hierarchical clustering highlights various degree of polyphenol similarity between grape varieties that were in agreement with the genetic distance using clustering analyses of 22 microsatellite DNA markers. Metabolite correlation network suggested that several polyphenol subclasses were differently controlled. The present polyphenol metabotyping approach coupled to multivariate statistical analyses might assist grape selection programs to improve metabolites with both health-benefit potential and plant defense traits.

Analysis of Metabolites in Cabernet Sauvignon and Shiraz Dry Red Wines from Shanxi by 1H NMR Spectroscopy Combined with Pattern Recognition Analysis

Metabolomics technology based on proton nuclear magnetic resonance (1H NMR) spectroscopy combined with pattern recognition analysis was used to characterize the Cabernet Sauvignon and Shiraz dry red wines vinified in the Linfen of Shanxi Province, China, in 2016. The results showed that there was a very significant difference between the metabolites of Cabernet Sauvignon and Shiraz dry red wines from the area of Linfen. Compared with Shiraz dry red wines, Cabernet Sauvignon dry red wines contained higher levels of proline, valine, tartaric acid, citric acid, malic acid, gallic acid, β-glucose and ethyl acetate, whereas 2,3-butanediol, lactic acid, choline, glycerol, α-D-glucuronic acid, succinic acid and alanine were present in lower levels. Application of NMR spectroscopy combined with pattern recognition analysis showed the discriminative power between wine varietals from the same production area. The loading plot from partial least squares discriminant analysis (PLs-DA) indicated that the key biomarkers for this differentiation were proline, tartaric acid, glycerol, lactic acid, choline, succinic acid and gallic acid, which was consistent with the result of quantitative analysis.

Trends in Chemometrics: Food Authentication, Microbiology, and Effects of Processing

In the last decade, the use of multivariate statistical techniques developed for analytical chemistry has been adopted widely in food science and technology. Usually, chemometrics is applied when there is a large and complex dataset, in terms of sample numbers, types, and responses. The results are used for authentication of geographical origin, farming systems, or even to trace adulteration of high value-added commodities. In this article, we provide an extensive practical and pragmatic overview on the use of the main chemometrics tools in food science studies, focusing on the effects of process variables on chemical composition and on the authentication of foods based on chemical markers. Pattern recognition methods, such as principal component analysis and cluster analysis, have been used to associate the level of bioactive components with in vitro functional properties, although supervised multivariate statistical methods have been used for authentication purposes. Overall, chemometrics is a useful aid when extensive, multiple, and complex real-life problems need to be addressed in a multifactorial and holistic context. Undoubtedly, chemometrics should be used by governmental bodies and industries that need to monitor the quality of foods, raw materials, and processes when high-dimensional data are available. We have focused on practical examples and listed the pros and cons of the most used chemometric tools to help the user choose the most appropriate statistical approach for analysis of complex and multivariate data.

Cool-Climate Red Wines-Chemical Composition and Comparison of Two Protocols for ¹H-NMR Analysis

This study investigates the metabolome of 26 experimental cool-climate wines made from 22 grape varieties using two different protocols for wine analysis by proton nuclear magnetic resonance (¹H-NMR) spectroscopy. The wine samples were analyzed as-is (wet) and as dried samples. The NMR datasets were preprocessed by alignment and mean centering. No normalization or scaling was performed. The “wet” method preserved the inherent properties of the samples and provided a fast and effective overview of the molecular composition of the wines. The “dried” method yielded a slightly better sensitivity towards a broader range of the compounds present in wines. A total of 27 metabolites including amino acids, organic acids, sugars, and alkaloids were identified in the ¹H-NMR spectra of the wine samples. Principal component analysis was performed on both NMR datasets evidencing well-defined molecular fingerprints for ‘Baco Noir’, ‘Bolero’, ‘Cabernet Cantor’, ‘Cabernet Cortis’, ‘Don Muscat’, ‘Eszter’, ‘Golubok’, ‘New York Muscat’, ‘Regent’, ‘Rondo’, ‘Triomphe d’Alsace’, ‘Précose Noir’, and ‘Vinoslivy’ wines. Amongst the identified metabolites, lactic acid, succinic acid, acetic acid, gallic acid, glycerol, and methanol were found to drive sample groupings. The ¹H-NMR data was compared to the absolute concentration values obtained from a reference Fourier transform infrared method, evidencing a high correlation.