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

Recent Advancement in NMR Based Plant Metabolomics: Techniques, Tools, and Analytical Approaches

Plant metabolomics, a rapidly advancing field within plant biology, is dedicated to comprehensively exploring the intricate array of small molecules in plant systems. This entails precisely gathering comprehensive chemical data, detecting numerous metabolites, and ensuring accurate molecular identification. Nuclear magnetic resonance (NMR) spectroscopy, with its detailed chemical insights, is crucial in obtaining metabolite profiles. Its widespread application spans various research disciplines, aiding in comprehending chemical reactions, kinetics, and molecule characterization. Biotechnological advancements have further expanded NMR's utility in metabolomics, particularly in identifying disease biomarkers across diverse fields such as agriculture, medicine, and pharmacology. This review covers the stages of NMR-based metabolomics, including historical aspects and limitations, with sample preparation, data acquisition, spectral processing, analysis, and their application parts.

A General Strategy for Food Traceability and Authentication Based on Assembly-Tunable Fluorescence Sensor Arrays

Food traceability and authentication systems play an important role in ensuring food quality and safety. Current techniques mainly rely on direct measurement by instrumental analysis, which is usually designed for one or a group of specific foods, not available for various food categories. To develop a general strategy for food identification and discrimination, a novel method based on fluorescence sensor arrays is proposed, composed of supramolecular assemblies regulated by non-covalent interactions as an information conversion system. The stimuli-responsiveness and tunability of supramolecular assemblies provided an excellent platform for interacting with various molecules in different foods. In this work, five sensor arrays constructed by supramolecular assemblies composed of pyrene derivatives and perylene derivatives are designed and prepared. Assembly behavior and sensing mechanisms are investigated systematically by spectroscopy techniques. The traceability and authentication effects on several kinds of food from different origins or grades are evaluated and verified by linear discriminant analysis (LDA). It is confirmed that the cross-reactive signals from different sensor units encompassing all molecular interactions can generate a unique fingerprint pattern for each food and can be used for traceability and authentication toward universal food categories with 100% accuracy.

Deep reinforcement learning classification of sparkling wines based on ICP-MS and DOSY NMR spectra

An approach that combines NMR spectroscopy and inductively coupled plasma mass spectrometry (ICP-MS) and advanced tensor decomposition algorithms with state-of-the-art deep learning procedures was applied for the classification of Croatian continental sparkling wines by their geographical origin. It has been demonstrated that complex high-dimensional NMR or ICP-MS data cannot be classified by higher-order tensor decomposition alone. Extension of the procedure by deep reinforcement learning resulted in an exquisite neural network predictive model for the classification of sparkling wines according to their geographical origin. A network trained on half of the sample set was able to classify even 94% of all samples. The model can particularly be useful in cases where the number of samples is limited and when simpler statistical methods fail to produce reliable data. The model can further be exploited for the identification and differentiation of sparkling wines including a high potential for authenticity or quality control.

Chemical Characterization of Red Wine Polymers and Their Interaction Affinity with Odorants

In order to characterize red wine polymers with regard to their binding properties to aroma compounds (odorants), a qualitative and quantitative analysis of chemical degradation products after different chemical treatments (thiolytic, acidic, and alkaline depolymerization) of high -molecular-weight (HMW) fractions of red wine was performed. Using 1H NMR, LC-ToF-MS, LC-MS/MS, and HPIC revealed key structural features such as carbohydrates, organic acids, phenolic compounds, anthocyanins, anthocyanidins, amino acids, and flavan-3-ols responsible for odorant-polymer interactions. Further, NMR-based interaction studies of the selected aroma compounds 3-methylbutanol, cis-whisky lactone, 3-methylbutanoic acid, and 3-isobutyl-2-methoxypyrazine with HMW polymers after chemical treatment demonstrated a reduced interaction affinity of the polymer compared to the native HMW fractions, and further, the importance of aromatic compounds such as flavan-3-ols for the formation of odorant polymer interactions. In addition, these observations could be verified by human sensory experiments. For the first time, the combination of a compositional analysis of red wine polymers and NMR-based interaction studies with chemically treated HMW fractions enabled the direct analysis of the correlation of the polymer's structure and its interaction affinity with key odorants in red wine.

1H NMR-based approach to determine the geographical origin and cultivation method of roasted coffee

A comprehensive study of 603 roasted arabica coffee samples using NMR fingerprinting and multivariate data analysis was performed to differentiate coffee samples according to their geographical origin and cultivation method. Both lipophilic and hydrophilic coffee metabolites were recorded using 1H NMR spectroscopy, and principal component analysis followed by linear discriminant analysis (PCA-LDA) was applied. Coffee samples were fist differentiated according to their continents of origin followed by discrimination of coffee samples from Brazil, Ethiopia, and Colombia from coffee samples originating from another continent. Discrimination of coffee samples according to their continent of origin and additional assignment to the countries Brazil and Ethiopia were successful. However, an unambiguous separation of Colombian coffee samples from coffee samples of another continent (other than South America) was not possible. Also, differentiation of organically and conventionally produced coffee samples by using 1H NMR and PCA-LDA was not achieved.

Rapid authentication of red wine by MALDI-MS combined with DART-MS

A simple, rapid and high-throughput approach was developed for authentication of red wine for the first time, by combining spectral results from matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and direct analysis in real time mass spectrometry (DART-MS). By coupling with orthogonal partial least squares discrimination analysis (OPLS-DA), this approach enabled successful classification of 535 wines from 8 countries, with the correct classification rates of 100% on the calibration set and over 90% on the validation set for almost all countries, and 26 potential characteristic markers selected. Compared to one single technique, this approach allowed detection of more compound ions, and with better fitting and predictive performances. The satisfactory differentiation results of vintages and grape varieties further verified the robustness of the approach. This study demonstrated the feasibility of combining multiple mass spectrometric techniques for wine analysis, which can be extended to other fields or to combinations of other analytical techniques.

From Nontargeted to Targeted Analysis: Feature Selection in the Differentiation of Truffle Species (Tuber spp.) Using 1H NMR Spectroscopy and Support Vector Machine

The price of different truffle types varies according to their culinary value, sometimes by more than a factor of 10. Nonprofessionals can hardly distinguish visually the species within the white or black truffles, making the possibility of food fraud very easy. Therefore, the identification of different truffle species (Tuber spp.) is an analytical task that could be solved in this study. The polar extract from a total of 80 truffle samples was analyzed by 1H NMR spectroscopy in combination with chemometric methods covering five commercially relevant species. All classification models were validated applying a repeated nested cross-validation. In direct comparison, the two very similar looking and closely related black representatives Tuber melanosporum and Tuber indicum could be classified 100% correctly. The most expensive truffle Tuber magnatum could be distinguished 100% from the other relevant white truffle Tuber borchii. In addition, signals for a potential Tuber borchii and a potential Tuber melanosporum marker for targeted approaches could be detected, and the corresponding molecules were identified as betaine and ribonate. A model covering all five truffle species Tuber aestivum, Tuber borchii, Tuber indicum, Tuber magnatum, and Tuber melanosporum was able to correctly discriminate between each of the species.

Chemical Profiling of Wines Produced in Bulgaria and Distinction from International Grape Varieties

Distinguishing the botanical and geographical origin of wine is important to prevent wine adulteration and to determine its quality. The combined use of ¹H NMR profiling and chemometrics allows the quantification of 31 common organic components in the NMR spectra of 70 wines from different sources. Using the NMR metabolomics approach, a successful differentiation of wines produced from Bulgarian and international grape varieties is achieved using linear discriminant analysis. Wines produced from typical local grape varieties contain higher average amounts of galacturonic, malic, tartaric, and succinic acid, alanine, choline, several alcohols, and saccharides arabinose, galactose, and sucrose than imported wine assortments. A practical decision tree is proposed for distinguishing 15 different grape varieties based on the amounts of the common wine components. An example of distinction of real from diluted wine via creation of a PLS-DA model is presented. Wines from the two subregions officially recognized by the EU at the Protected Geographical Indication (PGI) level are unequivocally recognized.

1H NMR-Based Metabolomics to Assess the Impact of Soil Type on the Chemical Composition of Nero d'Avola Red Wines

In this study, the soil effect on the micro-component composition of Nero d'Avola wines obtained from different locations was investigated through ¹H NMR-based metabolomics. Two different approaches were applied: the targeted (TA) and the non-targeted one (NTA). The former differentiated the wines by profiling (i.e., by identifying and quantifying) a number of different metabolites. The latter provided wine fingerprinting by processing the entire spectra with multivariate statistical analysis. NTA also allowed investigation of the hydrogen bond network inside wines via the analysis of ¹H NMR chemical shift dispersions. Results showed that the differences among wines were due not only to the concentrations of various analytes but also to the characteristics of the H-bond network where different solutes were involved. The H-bond network affects both gustatory and olfactory perceptions by modulating the way how solutes interact with the human sensorial receptors. Moreover, the aforementioned H-bond network is also related to the soil properties from which the grapes were taken. Therefore, the present study can be considered a good attempt to investigate terroir, i.e., the relationship between wine quality and soil characteristics.

Metabolomics integrated with machine learning to discriminate the geographic origin of Rougui Wuyi rock tea

The geographic origin of agri-food products contributes greatly to their quality and market value. Here, we developed a robust method combining metabolomics and machine learning (ML) to authenticate the geographic origin of Wuyi rock tea, a premium oolong tea. The volatiles of 333 tea samples (174 from the core region and 159 from the non-core region) were profiled using gas chromatography time-of-flight mass spectrometry and a series of ML algorithms were tested. Wuyi rock tea from the two regions featured distinct aroma profiles. Multilayer Perceptron achieved the best performance with an average accuracy of 92.7% on the training data using 176 volatile features. The model was benchmarked with two independent test sets, showing over 90% accuracy. Gradient Boosting algorithm yielded the best accuracy (89.6%) when using only 30 volatile features. The proposed methodology holds great promise for its broader applications in identifying the geographic origins of other valuable agri-food products.

Application of fuzzy algorithms in conjunction with 1 H-NMR spectroscopy to differentiate alcoholic beverages

BACKGROUND

Recent statistics from the European Commission indicate that wine is one of the commodities most commonly subject to food fraud. In this context, the development of reliable classification models to differentiate alcoholic beverages requires, besides sensitive analytical tools, the use of the most suitable data-processing methods like those based on advanced statistical tools or artificial intelligence.

RESULTS

The present study aims to establish a new, innovative approach for the differentiation of alcoholic beverages (wines and fruit distillates), which is able to increase the discrimination rate of the models that have been developed. A data dimensionality reduction step was applied to proton nuclear magnetic resonance (¹ H-NMR) profiles. This stage consisted of the application of fuzzy principal component analysis (FPCA) prior to the development of classification models through discriminant analysis. The enhancement of the model's classification potential by the application of FPCA in comparison with principal component analysis (PCA) was discussed.

CONCLUSION

The association of ¹ H-NMR spectroscopy and an appropriate statistical approach provided a very effective tool for the differentiation of alcoholic beverages. To develop reliable metabolomic approaches for the differentiation of wines and fruit distillates, ¹ H-NMR spectroscopic data were exploited in conjunction with fuzzy algorithms to reduce data dimensionality. The study proved the greater efficiency of using FPCA scores in comparison with those obtained through the widely applied PCA. The proposed approach enabled wines to be distinguished perfectly according to their geographical origins, cultivar, and vintage, and this could be used for wine classification. Moreover, 100% correctly classified samples were also achieved for the botanical and geographical differentiation of fruit distillates. © 2022 Society of Chemical Industry.

Current trends in ŒNO-NMR based metabolomics

Present work discusses strengths and limitations of two Nuclear Magnetic Resonance outliers obtained with a water-to-ethanol solvent multi pre saturation acquisition method, recently included in the Compendium of International Methods of Analysis of Wines and Musts, published as OIV-MA-AS316-01, and their accuracy for metabolomics analysis. Furthermore, it is also presented an alternative to produce more discriminant and sensitive NMR data matrices for metabolomics studies, comprising the use of a novel NMR acquisition strategy in wines, the double pulsed-field gradient echo (DPFGE) NMR scheme, with a refocusing band-selective uniform-response pure-phase selective pulse, for a selective excitation of the 5-10 ppm chemical shift range of wine samples, that reveals novel broad aromatic 1H resonances, directly associated to complex polyphenols. Both aromatics and full binned OIV-MA-AS316-01,as well as the selective 5-10 ppm DPFGE NMR outliers were statistically analyzed with diverse non-supervised Principal Component Analysis (PCA) and supervised Partial Least Squares -Discriminant Analysis (PLS-DA), sparse (sPLS-DA) least squares- discriminant analysis, and orthogonal projections to latent structures discriminant analysis (OPLS-DA). Supervised multivariate statistical analysis of DPFGE and aromatics’ binned OIV-MA-AS316-01NMR data have shown their robustness to broadly discriminate geographical origins and narrowly differentiate between different fermentation schemes of wines from identical variety and region.

NMR-Based Approaches in the Study of Foods

In this review, the three different NMR-based approaches usually used to study foodstuffs are described, reporting specific examples. The first approach starts with the food of interest that can be investigated using different complementary NMR methodologies to obtain a comprehensive picture of food composition and structure; another approach starts with the specific problem related to a given food (frauds, safety, traceability, geographical and botanical origin, farming methods, food processing, maturation and ageing, etc.) that can be addressed by choosing the most suitable NMR methodology; finally, it is possible to start from a single NMR methodology, developing a broad range of applications to tackle common food-related challenges and different aspects related to foods.

Chitosan Film as a Replacement for Conventional Sulphur Dioxide Treatment of White Wines: A 1H NMR Metabolomic Study

Chitosan–genipin (Ch-Ge) films have been proposed for the replacement of sulfur dioxide (SO₂) in white wines preservation to circumvent the adverse health consequences caused by SO₂ intake. To assess the effects of different-sized Ch-Ge films (25 and 100 cm²) on wine composition compared to SO₂-treated and untreated wines, nuclear magnetic resonance metabolomics was applied. Relative to SO₂, 100 cm² films induced significant changes in the levels of organic acids, sugars, amino acids, 5-hydroxymethylfurfural, among other compounds, while 25 cm² films appeared to induce only small variations. The observed metabolite variations were proposed to arise from the mitigation of fermentative processes, electrostatic interactions between acids and the positively charged films and the promotion of Maillard and Strecker reactions. Qualitative sensory analysis showed that wines maintained overall appropriate sensory characteristics, with 100 cm² film treated wines showing slightly higher attributes. Based on these results, the possibility of using Ch-Ge films as a replacement for SO₂ treatment is discussed.

Prediction of chemical indicators for quality of Zanthoxylum spices from multi-regions using hyperspectral imaging combined with chemometrics

Fruits of Zanthoxylum bungeanum Maxim (Red “Huajiao,” RHJ) and Z. schinifolium Sieb. et Zucc. (Green “Huajiao,” GHJ) are famous spices around the world. Antioxidant capability (AOC), total alkylamides content (TALC) and volatile oil content (VOC) in HJ are three important quality indicators and lack rapid and effective methods for detection. Non-destructive, time-saving, and effective technology of hyperspectral imaging (HSI) combined with chemometrics was adopted to improve the indicators prediction in this study. Results showed that the three chemical indexes exhibited significant differences between different regions and varieties (P < 0.05). Specifically, the mass percentages of TALC were 11–22% in RHJ group and 21–36% in GHJ group. The mass percentages of VOC content were 23–31% and 16–24% in RHJ and GHJ groups, respectively. More importantly, these indicators could be well predicted based on the full or effective HSI wavelengths via model adaptive space shrinkage (MASS) and iteratively variable subset optimization (IVSO) selections combined with wavelet transform (WT) method for noise reduction. The best prediction results of AOC, TALC, and VOC indicators were achieved with the highest residual predictive deviation (RPD) values of 7.43, 7.82, and 3.73 for RHJ, respectively, and 6.82, 2.66, and 4.64 for GHJ, respectively. The above results highlight the great potential of HSI assisted with chemometrics in the rapid and effective prediction of chemical indicators of Zanthoxylum spices.

Differentiation of meat species of raw and processed meat based on polar metabolites using 1H NMR spectroscopy combined with multivariate data analysis

Meat species of raw meat and processed meat products were investigated by 1H NMR spectroscopy with subsequent multivariate data analysis. Sample preparation was based on aqueous extraction combined with ultrafiltration in order to reduce macromolecular components in the extracts. 1H NMR data was analyzed by using a non-targeted approach followed by principal component analysis (PCA), linear discrimination analysis (LDA), and cross-validation (CV) embedded in a Monte Carlo (MC) resampling approach. A total of 379 raw meat samples (pork, beef, poultry, and lamb) and 81 processed meat samples (pork, beef, poultry) were collected between the years 2018 and 2021. A 99% correct prediction rate was achieved if the raw meat samples were classified according to meat species. Predicting processed meat products was slightly less successful (93 %) with this approach. Furthermore, identification of spectral regions that are relevant for the classification via polar chemical markers was performed. Finally, data on polar metabolites were fused with previously published 1H NMR data on non-polar metabolites in order to build a broader classification model and to improve prediction accuracy.

Untargeted metabolomic analysis by ultra-high-resolution mass spectrometry for the profiling of new Italian wine varieties

The chemical composition of wine samples comprises numerous bioactive compounds responsible for unique flavor and health-promoting properties. Thus, it's important to have a complete overview of the metabolic profile of new wine products in order to obtain peculiar information in terms of their phytochemical composition, quality, and traceability. To achieve this aim, in this work, a mass spectrometry-based phytochemical screening was performed on seven new wine products from Villa D'Agri in the Basilicata region (Italy), i.e., Aglianico Bianco, Plavina, Guisana, Giosana, Malvasia ad acino piccolo, Colata Murro and Santa Sofia. Ultra-high-resolution mass spectrometry data were processed into absorption mode FT-ICR mass spectra, in order to remove artifacts and achieve a higher resolution and lower levels of noise. Accurate mass-to-charge ratio (m/z) values were converted into putative elemental formulas. Therefore, 2D van Krevelen diagrams were used as a tool to obtain molecular formula maps useful to perform a rapid and more comprehensive analysis of the wine sample metabolome. The presence of important metabolite classes, i.e., fatty acid derivatives, amino acids and peptides, carbohydrates and phenolic derivatives, was assessed. Moreover, the comparison of obtained metabolomic maps revealed some differences among profiles, suggesting their employment as metabolic fingerprints. This study shed some light on the metabolic composition of seven new Italian wine varieties, improving their value in terms of related bioactive compound content. Moreover, different metabolomic fingerprints were obtained for each of them, suggesting the use of molecular maps as innovative tool to ascertain their unique metabolic profile.

NMR and Metabolomics-A Roadmap for the Future

Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021-the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.

1H NMR and UPLC-HRMS-based metabolomic approach for evaluation of the grape maturity and maceration time of Touriga Nacional wines and their correlation with the chemical stability

Touriga Nacional is a well-adapted Portuguese grape variety in São Francisco River Valley (northeastern Brazil). Nevertheless, it has only been indicated to short-term consumption because of the lack of chemical stability, which is attributed to low grape acidity and incomplete phenolic maturity. Therefore, we used Ultra-Performance Liquid Chromatography coupled High-resolution Mass Spectrometry, Nuclear Magnetic Resonance and chemometrics (PCA and PLS-DA) to evaluate the grape maturity and maceration time on chemical composition of wines from two harvest seasons. Moreover, we investigated how these experimental factors could affect their chemical stability. Grapes maturity showed to be the main effect. Overall, phenolic acids and short-chain organic acids were found to be at higher levels in wines produced with unripe grapes from February and shorter maceration time (p < 0.05). Proanthocyanidins and other flavonoids were increased in wines macerated for longer time using overripe grapes harvested in July. Furthermore, stable wines were made from overripe grapes, which contained more galacturonic acid.

Chemometric and sensometric techniques in enological data analysis

Enological evaluations capture the chemical and sensory space of wine using different techniques; many sensory methods as well as a variety of analytical chemistry techniques contribute to the amount of information generated. Data fusion, especially integrating data sets, is important when working with complex systems. The success reported when trying to integrate different modalities is generally low and has been attributed to the lack of statistically considerate strategies focusing on the data handling process. Multiple stages of data handling must be carefully considered when dealing with multi-modal data. In this review, the different stages in the data analysis process were examined. The study revealed misconceptions surrounding the process and elucidated rules for purpose-driven approaches by examining the complexities of each stage and the impact the decisions made at each stage have on the resulting models. The two major modeling approaches are either supervised (discrimination, classification, prediction) or unsupervised (exploration). Supervised approaches were emphatic on the pre-processing steps and prioritized increasing performance. Unsupervised approaches were mostly used for preliminary steps. The review found aspects often neglected when it came to the data collection and capturing which in the end contributed to the low success in combining sensory and chemistry data.

Nontargeted Analysis of Lipid Extracts Using 1H NMR Spectroscopy Combined with Multivariate Statistical Analysis to Discriminate between the Animal Species of Raw and Processed Meat

The animal species of raw meat and processed meat products was determined by ¹H NMR spectroscopy with subsequent multivariate data analysis. Sample preparation was based on comprehensive lipid extraction to capture nonpolar and polar (amphiphilic) fat components of meat. A nontargeted approach was used to analyze the ¹H NMR data, followed by a principal component analysis, linear discrimination analysis, and cross-validation embedded in a Monte Carlo re-sampling approach. A total of 437 raw meat samples (pork, beef, poultry, and lamb) and 81 processed meat samples (pork, beef, and poultry) were collected to build and/or test the classification model. On average, 98% of the analyzed raw meat samples and 97% of the processed meat products were correctly classified with respect to meat species. Furthermore, relevant spectral regions to identify potential chemical markers such as linoleic acids, trans-fatty acids, and cholesterol for the meat species classification were described.

Mass spectrometry in food authentication and origin traceability

Food authentication and origin traceability are popular research topics, especially as concerns about food quality continue to increase. Mass spectrometry (MS) plays an indispensable role in food authentication and origin traceability. In this review, the applications of MS in food authentication and origin traceability by analyzing the main components and chemical fingerprints or profiles are summarized. In addition, the characteristic markers for food authentication are also reviewed, and the advantages and disadvantages of MS-based techniques for food authentication, as well as the current trends and challenges, are discussed. The fingerprinting and profiling methods, in combination with multivariate statistical analysis, are more suitable for the authentication of high-value foods, while characteristic marker-based methods are more suitable for adulteration detection. Several new techniques have been introduced to the field, such as proton transfer reaction mass spectrometry, ambient ionization mass spectrometry (AIMS), and ion mobility mass spectrometry, for the determination of food adulteration due to their fast and convenient analysis. As an important trend, the miniaturization of MS offers advantages, such as small and portable instrumentation and fast and nondestructive analysis. Moreover, many applications in food authentication are using AIMS, which can help food authentication in food inspection/field analysis. This review provides a reference and guide for food authentication and traceability based on MS.

Application of Chitosan-Lignosulfonate Composite Coating Film in Grape Preservation and Study on the Difference in Metabolites in Fruit Wine

In order to solve the global problem of fruit rotting due to microbial infection and water loss after harvest, which leads to a large amount of food waste, this experiment uses degradable biological composite coating to prolong the preservation period of grapes. Chitosan (CH) and Lignosulfonate (LS) were used as Bio-based film materials, CH films, 1% CH/LS films and 2% CH/LS biomass composite films were synthesized by the classical casting method and applied to grape preservation packaging. Its preservation effect was tested by grape spoilage rate, water loss rate, hardness, soluble solids, titratable acid, and compared with plastic packaging material PE film. At the same time, 1H NMR technology combined with pattern recognition analysis (PCA) and partial least squares discriminant analysis (PLS-DA) was used to determine the nuclear magnetic resonance (NMR) of Cabernet Sauvignon, Chardonnay and Italian Riesling wines from the eastern foothills of Helan Mountain to explore the differences in metabolites of wine. The results of preservation showed that the grapes quality of CH films and 2% CH/LS coating package is better than the control group, the decay rates decreased from 37.71% to 21.63% and 18.36%, respectively, the hardness increased from 6.83 to 10.4 and 12.78 and the soluble solids increased from 2.1 in the control group to 3.0 and 3.2. In terms of wine metabolites, there are similar types of metabolites between cabernet Sauvignon dry red wine and Chardonnay and Italian Riesling dry white wine, but there are significant differences in content. The study found that 2% CH/LS coating package could not only reduce the spoilage rate of grapes, inhibit the consumption of soluble solids and titratable acids, but also effectively extend the shelf life of grapes by 6 days.

NMR Tracing of Food Geographical Origin: The Impact of Seasonality, Cultivar and Production Year on Data Analysis

The traceability of typical foodstuffs is necessary to protect high quality of traditional products. It is well-known that several factors could influence metabolites content in certified foods, but soil composition, altitude, latitude and coded production protocols constitute the territorial conditions responsible for the peculiar organoleptic and nutritional properties of labelled foods. Instead, regardless of origin, seasonality, cultivar, collection year can affect all agricultural products, so it is appropriate to include them in data analysis in order to obtain a correct interpretation of the differences linked to growing areas alone. Therefore, it is useful to use a flexible all-round technique, and NMR spectroscopy coupled with multivariate statistical analysis is considered a powerful means of assessing food authenticity. The purpose of this review is to investigate the relevance of year, cultivar, and seasonal period in the determination of food geographical origin using NMR spectroscopy. The strategy for testing these three factors may differ from author to author, but a preliminary study of cultivar or collection year effects on NMR spectra is the most popular method before starting the geographical characterization of samples. In summary, based on the available literature, the most significant influence is due to cultivar, followed by harvesting year, however seasonality is not considered a source of variability in data analysis.

Quantitative analysis of wine and other fermented beverages with benchtop NMR

We present a fully automated approach for quantitative compositional analysis of fermented beverages using benchtop nuclear magnetic resonance (NMR) spectroscopy. NMR spectroscopy, renowned for its applications in chemical structure elucidation, is gaining attention as a quantitative analytical technique due to its inherent linearity and the ability to obtain comprehensive quantitative information with a single simple experiment. Recently developed benchtop NMR spectrometers offer the quantitative capabilities of NMR to a wide range of potential users in industry, but their applicability has been limited by the reduced effective spectral resolution and the need for more advanced data processing. We address this problem with a model-based algorithm that hinges on the well-understood description of quantum mechanical phenomena in NMR spectroscopy. We demonstrate the effectiveness of our approach on a challenging problem of analysing the composition of wine and related fermented beverages - an important potential niche application of quantitative NMR. We successfully quantify more than 15 major components in the wine matrix and enable the quantification of species whose analysis is generally not possible with established methods. The average discrepancy of the obtained concentrations, when compared to the traditional methods of analysis, usually does not exceed 10% and is lower for the most abundant species (e.g. below 5% for ethanol).

NMR spectroscopy of wastewater: A review, case study, and future potential

NMR spectroscopy is arguably the most powerful tool for the study of molecular structures and interactions, and is increasingly being applied to environmental research, such as the study of wastewater. With over 97% of the planet's water being saltwater, and two thirds of freshwater being frozen in the ice caps and glaciers, there is a significant need to maintain and reuse the remaining 1%, which is a precious resource, critical to the sustainability of most life on Earth. Sanitation and reutilization of wastewater is an important method of water conservation, especially in arid regions, making the understanding of wastewater itself, and of its treatment processes, a highly relevant area of environmental research. Here, the benefits, challenges and subtleties of using NMR spectroscopy for the analysis of wastewater are considered. First, the techniques available to overcome the specific challenges arising from the nature of wastewater (which is a complex and dilute matrix), including an examination of sample preparation and NMR techniques (such as solvent suppression), in both the solid and solution states, are discussed. Then, the arsenal of available NMR techniques for both structure elucidation (e.g., heteronuclear, multidimensional NMR, homonuclear scalar coupling-based experiments) and the study of intermolecular interactions (e.g., diffusion, nuclear Overhauser and saturation transfer-based techniques) in wastewater are examined. Examples of wastewater NMR studies from the literature are reviewed and potential areas for future research are identified. Organized by nucleus, this review includes the common heteronuclei (¹³C, ¹⁵N, ¹⁹F, ³¹P, ²⁹Si) as well as other environmentally relevant nuclei and metals such as ²⁷Al, ⁵¹V, ²⁰⁷Pb and ¹¹³Cd, among others. Further, the potential of additional NMR methods such as comprehensive multiphase NMR, NMR microscopy and hyphenated techniques (for example, LC-SPE-NMR-MS) for advancing the current understanding of wastewater are discussed. In addition, a case study that combines natural abundance (i.e. non-concentrated), targeted and non-targeted NMR to characterize wastewater, along with in vivo based NMR to understand its toxicity, is included. The study demonstrates that, when applied comprehensively, NMR can provide unique insights into not just the structure, but also potential impacts, of wastewater and wastewater treatment processes. Finally, low-field NMR, which holds considerable future potential for on-site wastewater monitoring, is briefly discussed. In summary, NMR spectroscopy is one of the most versatile tools in modern science, with abilities to study all phases (gases, liquids, gels and solids), chemical structures, interactions, interfaces, toxicity and much more. The authors hope this review will inspire more scientists to embrace NMR, given its huge potential for both wastewater analysis in particular and environmental research in general.

Nontargeted 1 H NMR fingerprinting and multivariate statistical analysis for traceability of Greek PDO Vostizza currants

In this study, non-targeted ¹ H NMR fingerprinting was used in combination with multivariate statistical analyses for the classification of Greek currants based on their geographical origins (Aeghion, Nemea, Kalamata, Zante, and Amaliada). As classification techniques, Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were carried out. To elucidate different components according to PDO (Protected Designation of Origin), products from Aeghion (Vostizza) were statistically compared with each one of the four other regions. PLS-DA plots ensure that currants from Kalamata, Nemea, Zante, and Amaliada are well classified with respect to the PDO currants, according to differences observed in metabolites. Results suggest that composition differences in carbohydrates, amino, and organic acids of currants are sufficient to discriminate them in correlation to their geographical origin. In conclusion, currants metabolites which mostly contribute to classification performance of such discriminant analysis model present a suitable alternative technique for currants traceability. The study results contribute information to the currants' metabolite fingerprinting by NMR spectroscopy and their geographical origin. PRACTICAL APPLICATION: This study presents an analytical approach for a high nutritional value Greek PDO product, Vostizza currant. A further research and implementation of this method in food industry, can be the key to food fraud incidents. Thus, application of this work opens up posibilities to "farm to table" mission.

Detecting Admixture to Mango Purée of the Alphonso Cultivar (Mangifera indica L. cv. Alphonso) by 1H-NMR Spectroscopy

Food authenticity is becoming increasingly important but challenges existing analytical methods. In this study, we analyze the mango cultivar Alphonso with regard to authenticity using 1H-NMR spectroscopy. This cultivar has been termed “the king of mangoes” due to its unique flavor. Regarding its metabolites however, little is known about unique constellations that allow for differentiation of the Alphonso cultivar. We find that the Alphonso cultivar is distinguished by high levels of niacin, trigonelline, and histidine but features relatively low levels of alanine. Furthermore, we develop a model based on the local outlier factor algorithm that effectively detects admixture of non-Alphonso cultivars to Alphonso purée. This task is highly challenging because we identified no metabolites that are unique or uniquely absent in the Alphonso cultivar compared to other mango cultivars analyzed in this study. Our model shows promising results on a test set: Admixtures consisting of 35% non-Alphonso and 65% Alphonso mango purée were uncovered with a sensitivity of 88%. At the same time, our model verified Alphonso samples with a good specificity of 86%.

Vibrational Spectroscopy Combined with Chemometrics as Tool for Discriminating Organic vs. Conventional Culture Systems for Red Grape Extracts

Food plants provide a regulated source of delivery of functional compounds, plant secondary metabolites production being also tissue specific. In grape berries, the phenolic compounds, flavonoids and non-flavonoids, are distributed in the different parts of the fruit. The aim of this study was to investigate the applicability of FTIR and Raman screening spectroscopic techniques combined with multivariate statistical tools to find patterns in red grape berry parts (skin, seeds and pulp) according to grape variety and vineyard type (organic and conventional). Spectral data were acquired and processed using the same pattern for each different berry part (skin, seeds and pulp). Multivariate analysis has allowed a separation between extracts obtained from organic and conventional vineyards for each grape variety for all grape berry parts. The innovative approach presented in this work is low-cost and feasible, being expected to have applications in studies referring to the authenticity and traceability of foods. The findings of this study are useful as well in solving a great challenge that producers are confronting, namely the consumers’ distrust of the organic origin of food products. Further analyses of the chemical composition of red grapes may enhance the capability of the method of using both vibrational spectroscopy and chemometrics for discriminating the hydroalcoholic extracts according to grape varieties.

A Review of Wine Authentication Using Spectroscopic Approaches in Combination with Chemometrics

In a global context where trading of wines involves considerable economic value, the requirement to guarantee wine authenticity can never be underestimated. With the ever-increasing advancements in analytical platforms, research into spectroscopic methods is thriving as they offer a powerful tool for rapid wine authentication. In particular, spectroscopic techniques have been identified as a user-friendly and economical alternative to traditional analyses involving more complex instrumentation that may not readily be deployable in an industry setting. Chemometrics plays an indispensable role in the interpretation and modelling of spectral data and is frequently used in conjunction with spectroscopy for sample classification. Considering the variety of available techniques under the banner of spectroscopy, this review aims to provide an update on the most popular spectroscopic approaches and chemometric data analysis procedures that are applicable to wine authentication.

Impact of Global Climate Change on the European Barley Market Requires Novel Multi-Method Approaches to Preserve Crop Quality and Authenticity

Most recently in 2018 and 2019, large parts of Europe were affected by periods of massive drought. Resulting losses in cereal yield pose a major risk to the global supply of barley, as more than 60% of global production is based in Europe. Despite the arising price fluctuations on the cereal market, authenticity of the crop must be ensured, which includes correct declaration of harvest years. Here, we show a novel approach that allows such differentiation for spring barley samples, which takes advantage of the chemical changes caused by the extreme drought. Samples from 2018 were successfully differentiated from those of 2017 by analysis of changes in near-infrared spectra, enrichment in the isotope ¹³C, and strong accumulation of the plant-physiological marker betaine. We demonstrate that through consideration of multiple modern analysis techniques, not only can fraudulent labelling be prevented, but indispensable knowledge on the drought tolerance of crops can be obtained.

Nuclear Magnetic Resonance Metabolomics with Double Pulsed-Field-Gradient Echo and Automatized Solvent Suppression Spectroscopy for Multivariate Data Matrix Applied in Novel Wine and Juice Discriminant Analysis

The quality of foods has led researchers to use various analytical methods to determine the amounts of principal food constituents; some of them are the NMR techniques with a multivariate statistical analysis (NMR-MSA). The present work introduces a set of NMR-MSA novelties. First, the use of a double pulsed-field-gradient echo (DPFGE) experiment with a refocusing band-selective uniform response pure-phase selective pulse for the selective excitation of a 5–10-ppm range of wine samples reveals novel broad ¹H resonances. Second, an NMR-MSA foodomics approach to discriminate between wine samples produced from the same Cabernet Sauvignon variety fermented with different yeast strains proposed for large-scale alcohol reductions. Third a comparative study between a nonsupervised Principal Component Analysis (PCA), supervised standard partial (PLS-DA), and sparse (sPLS-DA) least squares discriminant analysis, as well as orthogonal projections to a latent structures discriminant analysis (OPLS-DA), for obtaining holistic fingerprints. The MSA discriminated between different Cabernet Sauvignon fermentation schemes and juice varieties (apple, apricot, and orange) or juice authentications (puree, nectar, concentrated, and commercial juice fruit drinks). The new pulse sequence DPFGE demonstrated an enhanced sensitivity in the aromatic zone of wine samples, allowing a better application of different unsupervised and supervised multivariate statistical analysis approaches.

High-resolution 1H NMR profiling of triacylglycerols as a tool for authentication of food from animal origin: Application to hen egg matrix

Metabolomics of complex biological matrices conducted by means of ¹H NMR leads to spectra suffering from severe signal overlapping. Previously, we have developed a high-resolution spectral treatment method to help solving this issue in ¹H NMR of triacylglycerols. In this work, we tested the potential of the developed method in the characterization and authentication of food products from animal origin using egg yolk as a model matrix. The approach consisted in a spectral deconvolution guided by the precision obtained on the deconvoluted peaks after reference lineshape adjustment of spectra. Thus, 135 peaks were quantitated and successfully used as biomarkers of origin, of hens breed, and of farming system. This required multivariate statistical analyses for classification. The same pool of variables allowed construction of multivariate quantitation models for individual fatty acids. Furthermore, minute amounts of conjugated fatty acids were quantitated and used as fingerprints of samples from backyard and free-range farming.

Comprehensive Study of Variety Oenological Potential Using Statistic Tools for the Efficient Use of Non-Renewable Resources

The evaluation of the variety suitability regarding each appellation’s specificities should be a strategy for maximizing the varieties’ oenological potential while contributing to the sustainable production of quality wines, keeping their typicity and rationalizing winemaking costs. Thus, the combination of several grape physicochemical attributes, modulated by climate and vineyard characteristics, providing knowledge for each grape variety’s oenological potential, is a relevant and reliable support for winemakers’ decisions. To prove this hypothesis, six mature grape varieties from three harvests, each one from three vineyard parcels with different topographical conditions from Bairrada Appellation (Portugal), were studied using analysis of variance–simultaneous components analysis (ASCA). The effects of harvest year and parcel on grape berry weight, pH, titratable acidity, total sugars, total phenolics, antiradical activity, and volatile composition in free and glycosidically-linked forms were analyzed. The compositional plasticity of autochthonous varieties (white Arinto and Bical and red Baga, Castelão, and Touriga Nacional) was observed. Sauvignon Blanc grape composition was significantly modulated by harvest. This study represents an important contribution for the maintenance of varieties’ biodiversity while contributing to establishing their peculiarities. Autochthonous varieties, if accurately exploited, can provide higher characteristic diversity than worldwide used varieties, an aspect to be more objectively taken into consideration by winemakers.

Vintage analysis of Chinese Baijiu by GC and 1H NMR combined with multivariable analysis

Economical-driven counterfeit and inferior aged Chinese Baijiu has caused serious concern of publicity in China. In this study, a total of 167 authentic Chinese Baijiu samples with different vintages including 3 flavor types were carefully collected. Gas chromatography (GC) was used to determine main volatile components and proton nuclear magnetic resonance (¹H NMR) spectroscopy was employed to obtain non-targeted fingerprints of Chinese Baijiu samples. Partial least squares regression (PLSR) models, which were confirmed by internal and external validation, were established for effectively identifying actual storage vintage of Chinese Baijiu with various brands, flavor types. Centering (Ctr), pareto scaling (Par), unit variance scaling (UV) data pretreatment methods, principal components (PCs), and three modified variable selection methods were proposed to successfully optimize the vintage model and effectively extract important vintage characteristic factors. This study demonstrated that NMR and GC combined with multivariate statistical analysis are effective tools for validating vintage authenticity of Chinese Baijiu.

Real wine or not? Protecting wine with traceability and authenticity for consumers: chemical and technical basis, technique applications, challenge, and perspectives

Wine is a high-value alcoholic beverage welcomed by consumers because of its flavor and nutritional value. The key information on wine bottle label is the basis of consumers' choice, which also becomes a target for manufacturers to adulterate, including geographical origin, grape variety and vintage. With the improvement of wine adulteration technology, modern technological means are needed to solve the above mentioned problems. The chemical basis of wine determines the type of technique used. Detection technology can be subdivided into four groups: mass spectrometry techniques, spectroscopic techniques, chromatography techniques, and other techniques. Multivariate statistical analysis of the data was performed by means of chemometrics methods. This paper outlines a series of procedures for wine classification and identification, and classified the analytical techniques and data processing methods used in recent years with listing their principles, advantages and disadvantages to help wine researchers choose appropriate methods to meet the challenge and ensure wine traceability and authenticity.

Fast Identification of Food Thickeners by Nontargeted NMR-Spectroscopy

Food thickeners are carbohydrate additives that can only be determined by long-term, multistep analysis. Fast methods to directly determine thickeners in food matrixes are therefore welcome. In this study, a rapid procedure based on the direct ¹H NMR analysis of food samples dissolved in deuterated water was developed. Individual thickeners were assigned due to specific marker signals gleaned from two-dimensional NMR analyses. The combination of one-dimensional ¹H NMR and DOSY experiments enabled unequivocal assignments of thickeners even in complex matrixes. Using this approach, gum arabic, carrageenan, agar-agar, galactomannans, and pectin could be identified in pastille, glaze, and fruit spread. Because of low concentrations (<0.5%-1%, w/w), the same thickeners and others such as xanthan gum and alginate could not be determined directly by NMR in curry sauce, rice pudding, choco milk drink, and lemon peel flavor. Moreover, NMR analyses of the hydrolysate did not reveal the specific monomeric units of the thickeners under study, as shown for the hydrolysate of lemon peel flavor. Nevertheless, the NMR approach could provide welcome means in the future to directly determine intact thickeners in food.

Combination of two analytical techniques improves wine classification by Vineyard, Region, and vintage

Three important wine parameters: vineyard, region, and vintage year, were evaluated using fifteen Vitis vinifera L. 'Pinot noir' wines derived from the same scion clone (Pinot noir 667). These wines were produced from two vintage years (2015 and 2016) and eight different regions along the Pacific Coast of the United States. We successfully improved the classification of the selected Pinot noir wines by combining an untargeted 1D ¹H NMR analysis with a targeted peptide based differential sensing array. NMR spectroscopy was used to evaluate the chemical fingerprint of the wines, whereas the peptide-based sensing array is known to mimic the senses of taste, smell, and palate texture by characterizing the phenolic profile. Multivariate and univariate statistical analyses of the combined NMR and differential sensing array dataset classified the genetically identical Pinot noir wines on the basis of distinctive metabolic signatures associated with the region of growth, vineyard, and vintage year.

Metabolomic understanding of the difference between unpruning and pruning cultivation of tea (Camellia sinensis) plants

Tea (Camellia sinensis) leaf quality depends on several factors such as plucking seasons, cultivation practices, and climatic conditions, which affect the chemical compositions of tea leaves. Pruning has been practiced as one of the common cultivation managements in tea cultivation and is hypothesized to exhibit metabolic differences from unpruned tea plants. Although metabolomics studies provide immense information about production of distinct tea products, the metabolic physiology of the plants cultivated under unpruning conditions is poorly understood. Therefore, in the present study, we explored the metabolic characteristics of tea leaves obtained from unpruned tea plants collected at different plucking seasons in a single year and in a given plucking time in the three successive years, through ¹H NMR-based metabolomics approach. Seasonal variations in diverse tea leaf metabolites both in pruned and unpruned tea plants were observed along with marked metabolic differences in tea leaves collected from pruned and unpruned tea plants in a given plucking time. Particularly, in abnormal year of vintage with high rainfall in 2018, high synthesis of glucose followed by high accumulations of catechin, including its derivatives, in unpruned tea, demonstrated intense active photosynthesis compared to pruned tea plants, indicating different metabolic responses of pruned and unpruned tea plants to similar climatic conditions. The current study highlights the important role of tea cultivation practices in tea plants for better management of leaf quality and the strong metabolic dependence on climatic conditions in a given vintage.