Targeted and nontargeted wine analysis by (1)h NMR spectroscopy combined with multivariate statistical analysis. Differentiation of important parameters: grape variety, geographical origin, year of vintage

Relaxometric learning: a pattern recognition method for T2 relaxation curves based on machine learning supported by an analytical framework

Nuclear magnetic resonance (NMR)-based relaxometry is widely used in various fields of research because of its advantages such as simple sample preparation, easy handling, and relatively low cost compared with metabolomics approaches. However, there have been no reports on the application of the T₂ relaxation curves in metabolomics studies involving the evaluation of metabolic mixtures, such as geographical origin determination and feature extraction by pattern recognition and data mining. In this study, we describe a data mining method for relaxometric data (i.e., relaxometric learning). This method is based on a machine learning algorithm supported by the analytical framework optimized for the relaxation curve analyses. In the analytical framework, we incorporated a variable optimization approach and bootstrap resampling-based matrixing to enhance the classification performance and balance the sample size between groups, respectively. The relaxometric learning enabled the extraction of features related to the physical properties of fish muscle and the determination of the geographical origin of the fish by improving the classification performance. Our results suggest that relaxometric learning is a powerful and versatile alternative to conventional metabolomics approaches for evaluating fleshiness of chemical mixtures in food and for other biological and chemical research requiring a nondestructive, cost-effective, and time-saving method.

1H-NMR metabolomics-based classification of Japanese sake and comparative metabolome analysis by gas chromatography-mass spectrometry

Six categories of Japanese sake have been established by the National Tax Agency of Japan. In this system, the rice polishing ratio and the addition of alcohol are the main criteria for classification. The most common nuclear magnetic resonance (NMR) spectrometry method is ¹H-NMR, and has higher throughput than gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS) analysis due to its short measurement time, easy sample preparation, and high reproducibility. However, owing to the production of dominant ethanol signals, metabolome analyses have not been used for classifying Japanese sake using ¹H-NMR. In this study, a technique to selectively suppress ethanol signals was used to classify Japanese sake by ¹H-NMR, and a model was constructed to predict the rice polishing ratio. The results were compared to those obtained by GC-MS. The suppression of ethanol signals enabled the detection of trace components by ¹H-NMR. In a principal component analysis (PCA) score plot of ¹H-NMR spectra with ethanol signal suppression, PC1 was associated with both the addition of alcohol and the rice polishing ratio. Additionally, the separation of samples observed was similar when PCA score plots of ¹H-NMR and GC-MS data were compared. Similarly, to predict the rice polishing ratio using partial least squares regression analysis, a model was constructed using ¹H-NMR data, and showed nearly similar values for precision and predictive performance with the model constructed using GC-MS data. These results suggest that metabolomic analyses of Japanese sake based on ¹H-NMR spectral patterns may be useful for classification.

NMR spectroscopy in wine authentication: An official control perspective

Wine authentication is vital in identifying malpractice and fraud, and various physical and chemical analytical techniques have been employed for this purpose. Besides wet chemistry, these include chromatography, isotopic ratio mass spectrometry, optical spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy, which have been applied in recent years in combination with chemometric approaches. For many years, ² H NMR spectroscopy was the method of choice and achieved official recognition in the detection of sugar addition to grape products. Recently, ¹ H NMR spectroscopy, a simpler and faster method (in terms of sample preparation), has gathered more and more attention in wine analysis, even if it still lacks official recognition. This technique makes targeted quantitative determination of wine ingredients and nontargeted detection of the metabolomic fingerprint of a wine sample possible. This review summarizes the possibilities and limitations of ¹ H NMR spectroscopy in analytical wine authentication, by reviewing its applications as reported in the literature. Examples of commercial and open-source solutions combining NMR spectroscopy and chemometrics are also examined herein, together with its opportunities of becoming an official method.

Comparative NMR Metabolomics Profiling between Mexican Ancestral & Artisanal Mezcals and Industrialized Wines to Discriminate Geographical Origins, Agave Species or Grape Varieties and Manufacturing Processes as a Function of Their Quality Attributes

The oenological industry has benefited from the use of Nuclear Magnetic Resonance (¹H-NMR) spectroscopy in combination with Multivariate Statistical Analysis (MSA) as a foodomics tool for retrieving discriminant features related to geographical origins, grape varieties, and further quality controls. Said omics methods have gained such attention that Intergovernmental Organizations and Control Agencies are currently recommending their massive use amongst countries as quality compliances for tracking standard and degradation parameters, fermentation products, polyphenols, amino acids, geographical origins, appellations d’origine contrôlée and type of monovarietal strains in wines. This study presents, for the first time, a ¹H-NMR/MSA profiling of industrial Mexican wines, finding excellent statistical features to discriminate between oenological regions and grape varieties with supervised Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA). In a comparative way, it is applied with the ¹H-NMR/OPLS-DA workflow for the first time in ancestral and artisanal Mexican mezcals with promising results to discriminate between regions, agave species and manufacturing processes. The central aim of this comparative study is to extrapolate the know-how of wine-omics into the non-professionalized mezcal industry for establishing the NMR acquisition, preprocessing and statistical analysis basis to implement novel, non-invasive and highly reproducible regional, agave species and manufacturing-quality controls.

NMR in the Service of Wine Differentiation

NMR is a swift and highly reproducible spectrometric technique that makes it possible to obtain spectra containing a lot of information about the sample analyzed. This approach helps major components be described in complex mixtures such as wine in just one analysis. Analysis of wine metabolites is very often used to understand the impact of geographical origin or variety on wine quality. NMR is often used for tracing the geographical origin of wine. Research on NMR metabolic effects of geographical origin is of great importance as the high added value of wines results from compliance with state legislation on the protected denomination of origin (PDO) and protected geographical indication (PGI) for the administration of the appellation of wines. A review of NMR with emphasis on SNIF-NMR in the analysis of wine authenticity is given. SNIF-NMR remains a method of choice for the detection of wine chaptalization as it is the only approach which provides position-specific information on the origin of sugar in wine. However, the sample preparation step, which lacks major improvements since its conception, is strenuous and expensive, and suffers from drawbacks in terms of low sample throughput. Mainstream 1D and 2D NMR experiments provide a fast and affordable way to authenticate wine based on the geographical origin, vintage, and variety discrimination, and include a simple and non-destructive sample preparation step. With this approach, spectral data processing often represents a crucial step of the analysis. With properly performed NMR experiments good to excellent differentiation of wines from different vintages, regions, and varieties was achieved recently.

Comprehensive Classification and Regression Modeling of Wine Samples Using 1H NMR Spectra

Recently, ¹H NMR (nuclear magnetic resonance) spectroscopy was presented as a viable option for the quality assurance of foods and beverages, such as wine products. Here, a complex chemometric analysis of red and white wine samples was carried out based on their ¹H NMR spectra. Extreme gradient boosting (XGBoost) machine learning algorithm was applied for the wine variety classification with an iterative double cross-validation loop, developed during the present work. In the case of red wines, Cabernet Franc, Merlot and Blue Frankish samples were successfully classified. Three very common white wine varieties were selected and classified: Chardonnay, Sauvignon Blanc and Riesling. The models were robust and were validated against overfitting with iterative randomization tests. Moreover, four novel partial least-squares (PLS) regression models were constructed to predict the major quantitative parameters of the wines: density, total alcohol, total sugar and total SO₂ concentrations. All the models performed successfully, with R² values above 0.80 in almost every case, providing additional information about the wine samples for the quality control of the products. ¹H NMR spectra combined with chemometric modeling can be a good and reliable candidate for the replacement of the time-consuming traditional standards, not just in wine analysis, but also in other aspects of food science.

Determination of the Geographical Origin of Asparagus officinalis L. by 1H NMR Spectroscopy

Food authenticity concerning the geographical origin becomes increasingly important for consumers, food industries, and food authorities. In this study, nontargeted ¹H NMR metabolomics combined with machine learning methodologies was applied to successfully distinguish the geographical origin of 237 samples of white asparagus from Germany, Poland, The Netherlands, Spain, Greece, and Peru. Support vector classification of the geographical origin achieved an accuracy of 91.5% for the entire sample set and 87.8% after undersampling the majority class. Important regions of the spectra could be identified and assigned to potential chemical markers. A subset of samples was compared to isotope-ratio mass spectrometry (IRMS), an established method for the determination of origin of white asparagus in Germany. Here, SVM classification led to accuracies of 79.4% for NMR and 70.9% for IRMS. Finally, the classification of asparagus from different German regions was evaluated, and the influence of year and variety was analyzed.

Phenolic Compounds as Markers of Wine Quality and Authenticity

Targeted and untargeted determinations are being currently applied to different classes of natural phenolics to develop an integrated approach aimed at ensuring compliance to regulatory prescriptions related to specific quality parameters of wine production. The regulations are particularly severe for wine and include various aspects of the viticulture practices and winemaking techniques. Nevertheless, the use of phenolic profiles for quality control is still fragmented and incomplete, even if they are a promising tool for quality evaluation. Only a few methods have been already validated and widely applied, and an integrated approach is in fact still missing because of the complex dependence of the chemical profile of wine on many viticultural and enological factors, which have not been clarified yet. For example, there is a lack of studies about the phenolic composition in relation to the wine authenticity of white and especially rosé wines. This review is a bibliographic account on the approaches based on phenolic species that have been developed for the evaluation of wine quality and frauds, from the grape varieties (of V. vinifera and non vinifera), to the geographical origin, the vintage year, the winemaking process, and wine aging. Future perspectives on the role of phenolic compounds in different wine quality aspects, which should be still exploited, are also outlined.

Wine or Beer? Comparison, Changes and Improvement of Polyphenolic Compounds during Technological Phases

Wine and beer are nowadays the most popular alcoholic beverages, and the benefits of their moderate consumption have been extensively supported by the scientific community. The main source of wine and beer's antioxidant behavior are the phenolic substances. Phenolic compounds in wine and beer also influence final product quality, in terms of color, flavor, fragrance, stability, and clarity. Change in the quantity and quality of phenolic compounds in wine and beer depends on many parameters, beginning with the used raw material, its place of origin, environmental growing conditions, and on all the applied technological processes and the storage of the final product. This review represents current knowledge of phenolic compounds, comparing qualitative and quantitative profiles in wine and beer, changes of these compounds through all phases of wine and beer production are discussed, as well as the possibilities for increasing their content. Analytical methods and their importance for phenolic compound determination have also been pointed out. The observed data showed wine as the beverage with a more potent biological activity, due to a higher content of phenolic compounds. However, both of them contain, partly similar and different, phenolic compounds, and recommendations have to consider the drinking pattern, consumed quantity, and individual preferences. Furthermore, novel technologies have been developing rapidly in order to improve the polyphenolic content and antioxidant activity of these two beverages, particularly in the brewing industry.

Wine traceability and authenticity: approaches for geographical origin, variety and vintage assessment

The aim of this work is to identify and discuss physicochemical wine characteristics, to provide to some extent a link to the vintage, variety, and/or geographical origin. Bibliographic datasets were attempted to provide the main information for topic comprehension, identifying the sources of wine compositional variability and how these can be expressed in terms of the belonging categories. Since all the environmental and technological conditions which vineyard and wine are subjected are rarely known, different sources were inspected. Great importance was given to the study of isotopic composition because of its importance in food frauds detection history. The interaction of the plant genotype with the environmental conditions of the vintage is the main responsible for the wines organic and inorganic fraction variability in terms of both total and relative content. This phenotypical expression, together with human and abiotic variability sources, has been examined since it contains to some extent the information for the discrimination of wines according to their category. Recently, new proton nuclear magnetic resonance (1H NMR) spectroscopy techniques have been under study and, used concurrently to chemometric data management procedures, showed to be an interesting and promising tool for wine characterization according to both vintage and variety.

Investigating the Role of Antioxidant Compounds in Riboflavin-Mediated Photo-Oxidation of Methionine: A 1H-NMR Approach

Riboflavin (RF) is a well-known photosensitizer, responsible for the light-induced oxidation of methionine (Met) leading to the spoilage of wine. An NMR approach was used to investigate the role of gallic acid (GA) and sulfur dioxide (SO₂) in the RF-mediated photo-oxidation of Met. Water solutions of RF and Met, with and without GA or SO₂, were exposed to visible light for increasing time in both air and nitrogen atmospheres. Upon light exposure, a new signal appeared at 2.64 ppm that was assigned to the S(O)CH₃ moiety of methionine sulfoxide. Its formation rate was lower in a nitrogen atmosphere and even lower in the presence of GA, supporting the ability of this compound in quenching the singlet oxygen. In contrast, SO₂ caused relevant oxidation of Met, moderately observed even in the dark, making Met less available in donating electrons to RF. The competition of GA versus Met photo-oxidation was revealed, indicating effectiveness of this antioxidant against the light-dependent spoilage of wine. A pro-oxidant effect of SO₂ toward Met was found as a possible consequence of radical pathways involving oxygen.

Application of 1H Nuclear Magnetic Resonance Spectroscopy as Spirit Drinks Screener for Quality and Authenticity Control

Due to legal regulations, the rise of globalised (online) commerce and the need for public health protection, the analysis of spirit drinks (alcoholic beverages >15% vol) is a task with growing importance for governmental and commercial laboratories. In this article a newly developed method using nuclear magnetic resonance (NMR) spectroscopy for the simultaneous determination of 15 substances relevant to assessing the quality and authenticity of spirit drinks is described. The new method starts with a simple and rapid sample preparation and does not need an internal standard. For each sample, a group of 1H-NMR spectra is recorded, among them a two-dimensional spectrum for analyte identification and one-dimensional spectra with suppression of solvent signals for quantification. Using the Pulse Length Based Concentration Determination (PULCON) method, concentrations are calculated from curve fits of the characteristic signals for each analyte. The optimisation of the spectra, their evaluation and the transfer of the results are done fully automatically. Glucose, fructose, sucrose, acetic acid, citric acid, formic acid, ethyl acetate, ethyl lactate, acetaldehyde, methanol, n-propanol, isobutanol, isopentanol, 2-phenylethanol and 5-(hydroxymethyl)furfural (HMF) can be quantified with an overall accuracy better than 8%. This new NMR-based targeted quantification method enables the simultaneous and efficient quantification of relevant spirit drinks ingredients in their typical concentration ranges in one process with good accuracy. It has proven to be a reliable method for all kinds of spirit drinks in routine food control.

Regionality in Australian Pinot noir wines: A study on the use of NMR and ICP-MS on commercial wines

Wine quality and character are defined in part by the terroir in which the grapes are grown. Metabolomic techniques, such as nuclear magnetic resonance (NMR) spectroscopy and inductively coupled plasma mass spectrometry (ICP-MS), are used to characterise wines and to detect wine fraud in other countries but have not been extensively trialled in Australia. This paper describes the use of ICP-MS and NMR to characterise a selection of Pinot noir wines. Wines from varying terroirs could be somewhat distinguished by their mineral content using principal component analysis (PCA). PCA was able to separate wines by their Australian states more clearly than by region. Metabolomic analysis of the wines using NMR did not find any correlations with climate/daytime temperatures, or region. An analysis of coinertia suggested that the two datasets were not redundant, and it is proposed that ICP-MS data is the most useful for determining regionality.

1H NMR chemometric models for classification of Czech wine type and variety

A set of 917 wines of Czech origin were analysed using nuclear magnetic resonance spectroscopy (NMR) with the aim of building and evaluating multivariate statistical models and machine learning methods for the classification of 6 types based on colour and residual sugar content, 13 wine grape varieties and 4 locations based on ¹H NMR spectra. The predictive models afforded greater than 93% correctness for classifying dry and medium dry, medium, and sweet white wines and dry red wines. The trained Random Forest (RF) model classified Pinot noir with 96% correctness, Blaufränkisch 96%, Riesling 92%, Cabernet Sauvignon 77%, Chardonnay 76%, Gewürtztraminer 60%, Hibernal 60%, Grüner Veltliner 52%, Pinot gris 48%, Sauvignon Blanc 45%, and Pálava 40%. Pinot blanc and Chardonnay, varieties that are often mistakenly interchanged, were discriminated with 71% correctness. The findings support chemometrics as a tool for predicting important features in wine, particularly for quality assessment and fraud detection.

13C NMR-Based Chemical Fingerprint for the Varietal and Geographical Discrimination of Wines

A fast, economic, and eco-friendly methodology for the wine variety and geographical origin differentiation using ¹³C nuclear magnetic resonance (NMR) data in combination with machine learning was developed. Wine samples of different grape varieties cultivated in different regions in Greece were subjected to ¹³C NMR analysis. The relative integrals of the ¹³C spectral window were processed and extracted to build a chemical fingerprint for the characterization of each specific wine variety, and then subjected to factor analysis, multivariate analysis of variance, and k-nearest neighbors analysis. The statistical analysis results showed that the ¹³C NMR fingerprint could be used as a rapid and accurate indicator of the wine variety differentiation. An almost perfect classification rate based on training (99.8%) and holdout methods (99.9%) was obtained. Results were further tested on the basis of Cronbach's alpha reliability analysis, where a very low random error (0.30) was estimated, indicating the accuracy and strength of the aforementioned methodology for the discrimination of the wine variety. The obtained data were grouped according to the geographical origin of wine samples and further subjected to principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The PLS-DA and variable importance in projection (VIP) allowed the determination of a chemical fingerprint characteristic of each geographical group. The statistical analysis revealed the possibility of acquiring useful information on wines, by simply processing the ¹³C NMR raw data, without the need to determine any specific metabolomic profile. In total, the obtained fingerprint can be used for the development of rapid quality-control methodologies concerning wine.

Electrochemical Sensors Coupled with Multivariate Statistical Analysis as Screening Tools for Wine Authentication Issues: A Review

Consumers are increasingly interested in the characteristics of the products they consume, including aroma, taste, and appearance, and hence, scientific research was conducted in order to develop electronic senses devices that mimic the human senses. Thanks to the utilization of electroanalytical techniques that used various sensors modified with different electroactive materials coupled with pattern recognition methods, artificial senses such as electronic tongues (ETs) are widely applied in food analysis for quality and authenticity approaches. This paper summarizes the applications of electrochemical sensors (voltammetric, amperometric, and potentiometric) coupled with unsupervised and supervised pattern recognition methods (principal components analysis (PCA), linear discriminant analysis (LDA), partial least square (PLS) regression, artificial neural network (ANN)) for wine authenticity assessments including the discrimination of varietal and geographical origins, monitoring the ageing processes, vintage year discrimination, and detection of frauds and adulterations. Different wine electrochemical authentication methodologies covering the electrochemical techniques, electrodes types, functionalization sensitive materials and multivariate statistical analysis are emphasized and the main advantages and disadvantages of using the proposed methodologies for real applications were concluded.

Introducing a novel procedure for peak alignment in one-dimensional 1H-NMR spectroscopy: a prerequisite for chemometric analyses of wine samples

Shifted peak positions in 1H-nuclear magnetic resonance (1H-NMR) spectroscopy of wine samples are inevitably occurring mainly due to variations in the sample matrix, which consists of ethanol, glycerol, carbohydrates, acids, phenolic compounds, minerals, and aroma compounds. Slight variations in pH during sample preparation or fluctuations in instrumental factors may contribute to shifted peak positions that need to be corrected before subjecting the NMR data to chemometric techniques to ensure samples are compared on the correct chemical shift scale. In the current work, a novel procedure for correcting 1H-NMR spectroscopy peak positions was developed by mapping of the raw NMR spectra on a common chemical shift axis using a simple interpolation approach. The mapping allowed a substantial correction of peak positions and subsequently reduced the computational burden in further spectral processing. Fine-tuning of the peak alignments was carried out efficiently by interval-wisely applying the correlation optimized warping (COW) algorithm. Our preceding mapping approach enabled the use of substantially simpler alignment parameters of the COW algorithm, thereby accelerating the whole peak alignment process. The developed procedure may also be suitable for facilitating NMR analyses of other sample types, such as agricultural, clinical or pharmaceutical samples in targeted or untargeted analytical approaches.

Impact of Hanseniaspora Vineae in Alcoholic Fermentation and Ageing on Lees of High-Quality White Wine

Hanseniaspora vineae is an apiculate yeast that plays a significant role at the beginning of fermentation, and it has been studied for its application in the improvement of the aromatic profile of commercial wines. This work evaluates the use of H. vineae in alcoholic fermentation compared to Saccharomyces cerevisiae and in ageing on the lees process (AOL) compared to Saccharomyces and non-Saccharomyces yeasts. The results indicated that there were not significant differences in basic oenological parameters. H. vineae completed the fermentation until 11.9% v/v of ethanol and with a residual sugars content of less than 2 g/L. Different aroma profiles were obtained in the wines, with esters concentration around 90 mg/L in H. vineae wines. Regarding the AOL assay, the hydroalcoholic solutions aged with H. vineae lees showed significantly higher absorbance values at 260 (nucleic acids) and 280 nm (proteins) compared to the other strains. However, non-significant differences were found in the polysaccharide content at the end of the ageing process were found compared to the other yeast species, with the exception of Schizosaccharomyces pombe that released around 23.5 mg/L of polysaccharides in hydroalcoholic solution. The use of H. vineae by the wineries may be a viable method in fermentation and AOL to improve the quality of white wines.

Trunk Girdling Increased Stomatal Conductance in Cabernet Sauvignon Grapevines, Reduced Glutamine, and Increased Malvidin-3-Glucoside and Quercetin-3-Glucoside Concentrations in Skins and Pulp at Harvest

Girdling is a traditional horticultural practice applied at fruit set or other phenological stages, and is used mostly as a vine management. In grapevines, it is used primarily for table grapes to improve berry weight, sugar content, color, and to promote early harvest. The objective of this study was to evaluate the effect of trunk girdling applied at veraison, in 'Cabernet Sauvignon' wine grapes (Vitis vinifera L.), on agronomical and physiological parameters during vine development from the onset of ripening (veraison) to harvest, and additionally to quantify the effect of girdling on primary and secondary metabolism. Girdling was applied 146 days after pruning (dap) at veraison, when berry sampling for metabolomics and agronomical evaluations commenced, with a further three sampling dates until harvest, at 156 dap (30% maturation, 10 days after girdling-dag), 181 dap (70% maturation, 35 dag), and 223 dap (commercial harvest, 77 dag). Skin/pulp and seed tissues were extracted separately and metabolomics was performed using one-dimensional proton nuclear magnetic resonance (1D 1H NMR) spectroscopy and high performance liquid chromatography (HPLC-DAD). At harvest, girdling significantly increased stomatal conductance (gs) in vines, decreased glutamine concentrations, and increased anthocyanin and flavonol concentrations in the skin/pulp tissues of grape berries. Berry weight was reduced by 27% from 181 dap to harvest, and was significantly higher in grapes from girdled vines at 181 dap. Sugars, organic acids, and other amino acids in skin/pulp or seeds were not significantly different, possibly due to extra-fascicular phloem vessels transporting metabolites from leaves to the roots. Using a metabolomics approach, differences between skin/pulp and seeds tissues were meaningful, and a greater number of secondary metabolites in skin/pulp was affected by girdling than in seeds. Girdling is a simple technique that could easily be applied commercially on vine management to improve berry color and other phenolics in 'Cabernet Sauvignon' grapes.

Wine Authenticity and Traceability with the Use of FT-IR

Fourier transform infrared spectroscopy (FT-IR) has gained popularity in the wine sector due to its simplicity and ability to provide a wine’s fingerprint. For this reason, it is often used for authentication and traceability purposes with more than satisfactory results. In this review, an outline of the reasons why authenticity and traceability are important to the wine sector is given, along with a brief overview of the analytical methods used for their attainment; statistical issues and compounds, on which authentication usually is based, are discussed. Moreover, insight on the mode of action of FT-IR is given, along with successful examples from its use in different areas of interest for classification. Finally, prospects and challenges for suggested future research are given. For more accurate and effective analyses, the construction of a large database consisting of wines from different regions, varieties and winemaking protocols is suggested.

Effects of High Sugar Content on Fermentation Dynamics and Some Metabolites of Wine-Related Yeast Species Saccharomyces cerevisiae, S. uvarum and Starmerella bacillaris

Starmerella bacillaris (synonym Candida zemplinina) is an important non-Saccharomyces yeast in winemaking with valuable oenological properties, accompanying Saccharomyces species in sweet wine fermentation, and has been suggested also for application as combined starter culture in dry or sweet wines. In this study, the major metabolites and nitrogen utilization of these yeasts are evaluated in the musts with high or extremely high sugar concentration. The change in the metabolic footprint of Saccharomyces cerevisiae, Saccharomyces uvarum and Starmerella bacillaris strains was compared when they were present as pure cultures in chemically defined grape juice medium with 220 and 320 g/L of sugar, to represent a fully matured and an overripe grape. Surprisingly, the extreme sugar concentration did not result in a considerable change in the rate of sugar consumption; only a shift of the sugar consumption curves could be noticed for all species, especially for Starmerella bacillaris. At the extreme sugar concentration, Starmerella bacillaris showed excellent glycerol production, moderate nitrogen demand together with a noticeable proline utilisation. The change in the overall metabolite pattern of Starmerella bacillaris allowed clear discrimination from the change of the Saccharomyces species. In this experiment, the adequacy of this non-Saccharomyces yeast for co-fermentation in juices with high sugar concentration is highlighted. Moreover, the results suggest that Starmerella bacillaris has a more active adaptation mechanism to extremely high sugar concentration.

Yeast-Yeast Interactions: Mechanisms, Methodologies and Impact on Composition

During the winemaking process, alcoholic fermentation is carried out by a consortium of yeasts in which interactions occurs. The consequences of these interactions on the wine matrix have been widely described for several years with the aim of controlling the winemaking process as well as possible. In this review, we highlight the wide diversity of methodologies used to study these interactions, and their underlying mechanisms and consequences on the final wine composition and characteristics. The wide variety of matrix parameters, yeast couples, and culture conditions have led to contradictions between the results of the different studies considered. More recent aspects of modifications in the composition of the matrix are addressed through different approaches that have not been synthesized recently. Non-volatile and volatile metabolomics, as well as sensory analysis approaches are developed in this paper. The description of the matrix composition modification does not appear sufficient to explain interaction mechanisms, making it vital to take an integrated approach to draw definite conclusions on them.

Untargeted and targeted metabolomics strategy for the classification of strong aroma-type baijiu (liquor) according to geographical origin using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry

A metabolomics strategy was developed to differentiate strong aroma-type baijiu (SAB) (distilled liquor) from the Sichuan basin (SCB) and Yangtze-Huaihe River Basin (YHRB) through liquid-liquid extraction coupled with GC×GC-TOFMS. PCA effectively separated the samples from these two regions. The PLS-DA training model was excellent, with explained variation and predictive capability values of 0.988 and 0.982, respectively. As a result, the model demonstrated its ability to perfectly differentiate all the unknown SAB samples. Twenty-nine potential markers were located by variable importance in projection values, and twenty-four of them were identified and quantitated. Discrimination ability is closely correlated to the characteristic flavor compounds, such as acid, esters, furans, alcohols, sulfides and pyrazine. Most of the marker compounds were less abundant in the SCB samples than in the YHRB samples. The quantitated markers were further processed using hierarchical cluster analysis for targeted analysis, indicating that the markers had great discrimination power to differentiate the SAB samples.

Testing the limits of FT-Raman spectroscopy for wine authentication: Cultivar, geographical origin, vintage and terroir effect influence

FT-Raman spectroscopy represents an environmentally friendly technique, suitable for the analysis of high-water content food matrices, like wines, due to its relatively weak water bending mode in the fingerprint region. Based on metabolomics applied to FT-Raman spectra, this study presents the classifications achieved for a sample set comprising 126 wines, originated from Romania and France, with respect to cultivar, geographical origin and vintage. Cultivar recognition was successfully performed among four varieties (Sauvignon, Riesling, Chardonnay, Pinot Gris) while subtle particularities exiting between the Chardonnay wines, coming from the two countries, because of terroir influences were pointed out. The obtained separations of 100% in both initial and cross-validation procedure for geographical differentiation between the two origin countries, as well as, among the three Romanian areas (Transylvania, Muntenia and Moldova) were also discussed. Apart of this, the limitations and the importance of choosing a meaningful data set, in terms of representativity for each classification criterion, are addressed in the present work.

Complementary use of 1H NMR and multi-element IRMS in association with chemometrics enables effective origin analysis of cocoa beans (Theobroma cacao L.)

Within the cocoa market (Theobroma cacao L.), quality and prices are often determined by geographical origin, making traceability indispensable. Therefore, to investigate possibilities of tracing by analytical methods, 48 carefully selected cocoa samples from 20 countries have been profiled using a combination of stable isotope-ratio mass spectrometry (IRMS) and proton nuclear magnetic resonance (¹H NMR). Chemometric analysis of combined data sets from both, stable isotope data (δ¹³C, δ¹⁵N, δ¹⁸O, δ²H, %C, %N, %O, %H) and ¹H NMR fingerprints, achieved good separation with increased classification rates compared to classification with data of the isolated methods. IRMS contributed primarily to discrimination between countries, while ¹H NMR significantly contributed to separation of varieties, but also the regions within individual countries. This study thus demonstrates that combination of two analytical methods is an effective tool to enhance both, accuracy and precision, in authenticity testing of cocoa.

Application of Spectroscopic UV-Vis and FT-IR Screening Techniques Coupled with Multivariate Statistical Analysis for Red Wine Authentication: Varietal and Vintage Year Discrimination

One of the most important issues in the wine sector and prevention of adulterations of wines are discrimination of grape varieties, geographical origin of wine, and year of vintage. In this experimental research study, UV-Vis and FT-IR spectroscopic screening analytical approaches together with chemometric pattern recognition techniques were applied and compared in addressing two wine authentication problems: discrimination of (i) varietal and (ii) year of vintage of red wines produced in the same oenological region. UV-Vis and FT-IR spectra of red wines were registered for all the samples and the principal features related to chemical composition of the samples were identified. Furthermore, for the discrimination and classification of red wines a multivariate data analysis was developed. Spectral UV-Vis and FT-IR data were reduced to a small number of principal components (PCs) using principal component analysis (PCA) and then partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were performed in order to develop qualitative classification and regression models. The first three PCs used to build the models explained 89% of the total variance in the case of UV-Vis data and 98% of the total variance for FR-IR data. PLS-DA results show that acceptable linear regression fits were observed for the varietal classification of wines based on FT-IR data. According to the obtained LDA classification rates, it can be affirmed that UV-Vis spectroscopy works better than FT-IR spectroscopy for the discrimination of red wines according to the grape variety, while classification of wines according to year of vintage was better for the LDA based FT-IR data model. A clear discrimination of aged wines (over six years) was observed. The proposed methodologies can be used as accessible tools for the wine identity assurance without the need for costly and laborious chemical analysis, which makes them more accessible to many laboratories.

Red Wine Consumption and Cardiovascular Health

Wine is a popular alcoholic beverage that has been consumed for hundreds of years. Benefits from moderate alcohol consumption have been widely supported by the scientific literature and, in this line, red wine intake has been related to a lesser risk for coronary heart disease (CHD). Experimental studies and meta-analyses have mainly attributed this outcome to the presence in red wine of a great variety of polyphenolic compounds such as resveratrol, catechin, epicatechin, quercetin, and anthocyanin. Resveratrol is considered the most effective wine compound with respect to the prevention of CHD because of its antioxidant properties. The mechanisms responsible for its putative cardioprotective effects would include changes in lipid profiles, reduction of insulin resistance, and decrease in oxidative stress  of low-density lipoprotein cholesterol (LDL-C). The aim of this review is to summarize the accumulated evidence correlating moderate red wine consumption with prevention of CHD by focusing on the different mechanisms underlying this relationship. Furthermore, the chemistry of wine as well as chemical factors that influence the composition of the bioactive components of red wine are also discussed.

Rheo-NMR in food science-Recent opportunities

For over 25 years, nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) techniques have been used to study materials under mechanical deformation. Collectively, these methods are referred to as Rheo-NMR. In many cases, it provides spatially and temporally resolved maps of NMR spectra, intrinsic NMR parameters (such as relaxation times), or motion (such as diffusion or flow). Therefore, Rheo-NMR is complementary to conventional rheological measurements. This review will briefly summarize current capabilities and limitations of Rheo-NMR in the context of material science and food science in particular. It will report on recent advances such as the incorporation of torque sensors or the implementation of large amplitude oscillatory shear and point out future opportunities for Rheo-NMR in food science.

NMR-based differentiation of conventionally from organically produced chicken eggs in Germany

Both the German and European organic food markets are growing fast, and there is also a rising demand for organic chicken eggs. Consumers are willing to pay higher prices for organic eggs produced in an animal-appropriate environment considering animal welfare. Strict labelling requirements do not prevent chicken eggs from being a subject of food fraud. Conventionally produced (barn/free-range) eggs can easily be mislabeled as organic eggs. Especially because the demand for organically produced chicken eggs is likely to exceed supply in the future, mislabeling appears to be a realistic scenario. Therefore, there is a need for analytical methods that are suitable to classify eggs as being either conventionally or organically produced. Nuclear magnetic resonance (NMR) spectroscopy in combination with multivariate data analysis is a suitable tool to screen eggs according to the different systems of husbandry. Sample preparation is based on a fat extraction method, which was optimised for application to freeze-dried egg yolk. Samples were analysed using typical q-NMR parameters. A nontargeted approach was used for the analysis of the ¹ H NMR data. Principal component analysis (PCA) was applied followed by a linear discriminant analysis (PCA-LDA) and Monte Carlo cross-validation. In total, 344 chicken eggs (214 barn/free-range eggs and 130 eggs from organic farms), most of them originating from Germany, were used to build and validate the prediction model. The results showed that the prediction model allowed for the correct classification of about 93% of the organic eggs.

Characterization and classification of wines according to geographical origin, vintage and specific variety based on elemental content: a new chemometric approach

A highly informative chemometric approach using elemental data to distinguish and classify wine samples according to different criteria was successfully developed. The robust chemometric methods, such fuzzy principal component analysis (FPCA), FPCA combined with linear discriminant analysis (LDA), namely FPCA-LDA and mainly fuzzy divisive hierarchical associative-clustering (FDHAC), including also classical methods (HCA, PCA and PCA-LDA) were efficaciously applied for characterization and classification of white wines according to the geographical origin, vintage or specific variety. The correct rate of classification applying LDA was 100% in all cases, but more compact groups have been obtained for FPCA scores. A similar separation of samples resulted also when the FDHAC was employed. In addition, FDHAC offers an excellent possibility to associate each fuzzy partition of wine samples to a fuzzy set of specific characteristics, finding in this way very specific elemental contents and fuzzy markers according to the degrees of membership (DOMs).

1H NMR metabolomics applied to Bordeaux red wines

The q-NMR metabolomics has already demonstrated its potential for classifying wines of different geographical origins, grape varieties, or vintages. This study focuses on the characterisation of Bordeaux red wines, seeking to discriminate them from others produced in the major French wine regions. A sampling of 224 commercial French wines was analysed by ¹H NMR and forty compounds were quantified. Non-supervised and supervised statistical analyses revealed a singular imprint of Bordeaux wines in comparison with other French wines, with classification rates ranging from 71% to 100%. Within the Bordeaux vineyards, red wines from the different Bordeaux subdivisions were analysed from different vintages. Our results indicate that q-NMR metabolomics enables the differentiation of Médoc and Libournais vineyard highlighting the most discriminant constituents. In addition, the effects of wine evolution during bottle aging and vintage on Bordeaux red wines were pointed out and discussed.

Nontargeted Detection Methods for Food Safety and Integrity

Nontargeted workflows for chemical hazard analyses are highly desirable in the food safety and integrity fields to ensure human health. Two different analytical strategies, nontargeted metabolomics and chemical database filtering, can be used to screen unknown contaminants in food matrices. Sufficient mass and chromatographic resolutions are necessary for the detection of compounds and subsequent componentization and interpretation of candidate ions. Analytical chemistry–based technologies, including gas chromatography–mass spectrometry (GC-MS), liquid chromatography–mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and capillary electrophoresis–mass spectrometry (CE-MS), combined with chemometrics analysis are being used to generate molecular formulas of compounds of interest. The construction of a chemical database plays a crucial role in nontargeted detection. This review provides an overview of the current sample preparation, analytical chemistry–based techniques, and data analysis as well as the limitations and challenges of nontargeted detection methods for analyzing complex food matrices. Improvements in sample preparation and analytical platforms may enhance the relevance of food authenticity, quality, and safety.

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.

Quo vadis non-targeted wine analysis?

Important challenges of wine authentication - for example the proof of wine varieties, the determination of the vintage or the geographical origin, the determination of certain practices (e.g. differentiation of barrel/chips storage) become more and more successfully investigated by non-targeted analytical approaches. Not only in relation to these open questions in wine authentication non-targeted approaches such as nuclear magnetic resonance and mass spectrometry offer enormous potential also in wine authentication due to their typical abilities as high-through put and screening technique.

The adaptation from research into routine applications takes actually place in some instances (e.g. NMR) including wine analysis. In addition the combination of non-targeted and targeted approaches within the same run of analysis provides possibilities complementing or even substituting classical methodologies.

Some examples, the general workflow and typical evaluation strategies will be presented and their possibilities and limits discussed. First results of a recent study on the NMR analysis with focus on variety verification and reproducibility of spectral data will be presented. Particular emphasis will be put on general open questions such as exchangeability of data, validation, standardisation options.

The “Wine-T1” NMR experiment for novel wine-metabolome fingerprinting with nuclear-spin relaxation

In agreement with the draft resolution OENO-SCMA 17-618 at step 5 “Quantitation of glucose, malic acid, acetic acid, fumaric acid, shikimic acid and sorbic acid in wine using proton nuclear magnetic resonance spectroscopy (1H NMR)” said technique has been recently accepted within the OIV chair as a primary quantitative analytical technique for beverage analysis such as wine. However, poor chemical shift dispersion in 1H NMR spectra severely penalizes quantification within overlapped or crowded regions. To outflank said penalization and quantify metabolites in signal overcrowding situations, the novel “Wine-T1” experiment is proposed. The novel scheme comprises the addition of a second dimension, wherein the proton spin-lattice relaxation times (T1-{1H}) of each metabolite's spin-system is correlated to a chemical-shift dimension. The new experiment includes a water and ethanol signal pre-saturation module, prior to the T1 saturation-inversion recovery dimension in order to maximize signal-to-noise ratio of wine metabolome NMR spectra. “Wine-T1” pulse sequence can be adapted to all commercial spectrometers (Bruker, Varian/Agilent, Jeol) and with acquisition times in the order of minutes, it should be considered as a fast repetition method to produce a robust metabolome fingerprint that has not been described before, to the best of our knowledge.

Determination of Peptide Profile Consistency and Safety of Collagen Hydrolysates as Quality Attributes

Abstract

Collagen hydrolysates are dietary supplements used for nutritional and medical purposes. They are complex mixtures of low‐molecular‐weight peptides obtained from the enzymatic hydrolysis of collagen, which provide intrinsic batch‐to‐batch heterogeneity. In consequence, the quality of these products, which is related to the reproducibility of their mass distribution pattern, should be addressed. Here, we propose an analytical approach to determine the peptide pattern as a quality attribute of Colagenart®, a product containing collagen hydrolysate. In addition, we evaluated the safety by measuring the viability of two cell lines exposed to the product. The consistency of peptide distribution was determined using Size Exclusion Chromatography (SEC), Mass Spectrometry coupled to a reversed phase UPLC system (MS‐RP‐UPLC), and Shaped‐pulse off‐resonance water‐presaturation proton nuclear magnetic resonance spectrometry [¹Hwater_presat NMR]. The mass distribution pattern determined by SEC was in a range from 1.35 to 17 kDa, and from 2 to 14 kDa by MS‐RP‐UPLC. [¹Hwater_presat NMR] showed the detailed spin‐systems of the collagen hydrolysates components by global assignment of backbone Hα and NH, as well as side‐chain proton resonances. Additionally, short‐range intraresidue connectivity pathways of identified spin‐regions were obtained by a 2D homonuclear shift correlation Shaped‐pulse solvent suppression COSY scheme. Safety analysis of Colagenart® was evaluated in CaCo‐2 and HepG2 cells at 2.5 and 25 μg/mL and no negative effects were observed. The results demonstrated batch‐to‐batch reproducibility, which evinces the utility of this approach to establish the consistency of the quality attributes of collagen hydrolysates.

Practical Application

We propose state‐of‐the art analytical methodologies (SEC, MS, and NMR) to evaluate peptide profile and composition of collagen hydrolysates as quality attributes. These methodologies are suitable to be implemented for quality control purposes.

Revealing full chemical forms of lead in wine with combined XRF-NMR technologies

Since 1953, The World Organization of Vine and Wine (OIV) Member States have reduced the lead maximum limits (ML) in wines, down to 0.05 mg/L (2018). Evidently, this ML value is too restrictive for wine industry as it excludes from international market a significant portion of wine production. Currently, the Codex Committee on Contaminants in Foods and OIV had recognized the value of gathering robust and novel data to better assess the best lowest ML for wine industry. Currently, there is not a direct statement within international reference documents, of which chemical form of lead must be controlled and/ or reduced. This work presents for the first time a method combining Energy Dispersive X-Ray analysis (EDAX) and Nuclear Magnetic Resonance (NMR) spectroscopies in order to determine presence and concentrations of major and trace elements of lead and other element moieties in wine that can allow to better redefine lead's ML. By identification of K, L, M, radiation shells with additional αβi labelling of lead's major and minor components with semi-quantitative XRF, combined with chemical-shift analysis of inorganic Pb4+, Pb2+ and/or organo-lead within wine samples, we propose a full discrimination framework to disentangle and quantify different chemical forms of lead.

“One-shot” analysis of wine parameters in non-Saccharomyces large-scale alcohol reduction processes with one- and two-dimensional nuclear magnetic resonance

Facing climate change in wine industry comprises the implementation of strategies, such as to reduce alcohol in wines, promoted by abnormal increment of sugar levels in wine grapes. The present work discusses the first industrial-scale use of specific yeast strains able to produce wine with reduced alcoholic concentration. Reduction of alcohol content and quantification of key metabolites associated to oenological practice and/or quality were simultaneously measured in a “one-shot” way with proton Nuclear Magnetic Resonance Spectroscopy. Novel relevant metabolites were revealed with the use of a two-dimensional 1H-13C HSQC multipresat correlation spectroscopy, whereas a detailed methodological NMR description is stressed, towards revealing novel resonances within the NMR signature. The use of multitask analytical methods to simultaneously describe alcohol reduction and NMR targeting, completes the portfolio of NMR solutions recently proposed to the World Organisation of Vine and Wine for as well quantify aging and varieties.

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.

Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review

Nuclear magnetic resonance (NMR) spectroscopy is a robust method, which can rapidly analyze mixtures at the molecular level without requiring separation and/or purification steps, making it ideal for applications in food science. Despite its increasing popularity among food scientists, NMR is still an underutilized methodology in this area, mainly due to its high cost, relatively low sensitivity, and the lack of NMR expertise by many food scientists. The aim of this review is to help bridge the knowledge gap that may exist when attempting to apply NMR methodologies to the field of food science. We begin by covering the basic principles required to apply NMR to the study of foods and nutrients. A description of the discipline of chemometrics is provided, as the combination of NMR with multivariate statistical analysis is a powerful approach for addressing modern challenges in food science. Furthermore, a comprehensive overview of recent and key applications in the areas of compositional analysis, food authentication, quality control, and human nutrition is provided. In addition to standard NMR techniques, more sophisticated NMR applications are also presented, although limitations, gaps, and potentials are discussed. We hope this review will help scientists gain some of the knowledge required to apply the powerful methodology of NMR to the rich and diverse field of food science.