Verifying the red wines adulteration through isotopic and chromatographic investigations coupled with multivariate statistic interpretation of the data

Advancing Stable Isotope Analysis for Alcoholic Beverages' Authenticity: Novel Approaches in Fraud Detection and Traceability

BACKGROUND

Alcoholic beverages have been popular for thousands of years due to their unique flavors and cultural significance. However, the industry's high profit margins have led to increasingly sophisticated counterfeiting practices. Stable isotope analysis has emerged as one of the most promising techniques for addressing authenticity and traceability challenges in alcoholic beverages. Scope and approach: This review presents a comprehensive summary of the principles and recent advancements in the application of stable isotope techniques for authenticity assessment. It examines their use in detecting fraud (e.g., identifying edible alcohol, exogenous water, carbonylation, and trace compounds), vintage identification, and geographical origin determination across various alcoholic beverages, with a particular focus on wine, Chinese baijiu, and beer.

CONCLUSIONS

Stable isotope analysis is a powerful tool for verifying the authenticity of alcoholic beverages, offering effective solutions to combat counterfeiting, mislabeling, and adulteration. Future studies should focus on understanding the ecological, biological, and hydrometeorological factors influencing isotope signatures and develop advanced multi-isotope and chemometric approaches to improve reliability. Expanding global databases and integrating emerging technologies such as artificial intelligence (AI) and machine learning will further enhance the effectiveness and accessibility of stable isotope techniques, ensuring safer and higher-quality alcoholic beverages for consumers worldwide.

Combining Metal(loid) and Secondary Metabolite Levels in Olea europaea L. Samples for Geographical Identification

To fight counterfeits, and to protect the consumer, the interest in certifying the origin of agricultural goods has been growing in recent years. In this context and to increase the accuracy of zoning models, multiple analytical techniques must be combined via a multivariate approach. During the sampling campaign, leaves and fruits (olives or drupes) were collected from multiple orchards and farms. By means of HPLC-DAD, metabolite levels were evaluated and combined with the trace and ultra-trace metal/metalloid levels evaluated by ICP-MS (QqQ). The combined dataset was then used to develop a model for geographical traceability. Furthermore, the mineral content of the soil, evaluated by means of ICP-MS, was correlated with both the mineral content in the leaves and drupes and the metabolomic profiles to further investigate the connection between the orchard's location and characteristics of the final products.

Enhanced food authenticity control using machine learning-assisted elemental analysis

With the increasing attention being paid to the authenticity of food, efficient and accurate techniques that can solve relevant problems are crucial for improving public trust in food. This review explains two main aspects of food authenticity, namely food traceability and food quality control. More explicitly, they are the traceability of food origin and organic food, detection of food adulteration and heavy metals. It also points out the limitations of the commonly used morphology and organic compound detection methods, and highlights the advantages of combining the elements in food as detection indicators using machine learning technology to solve the problem of food authenticity. Taking elements as detection objects has the significant advantages of stability, machine learning technology can combine large data samples, ensuring both the accuracy and efficiency. In addition, the most suitable algorithm can be found by comparing their accuracy.

Determination of Elemental Content in Vineyard Soil, Leaves, and Grapes of Sangiovese Grapes from the Chianti Region Using ICP-MS for Geographical Identification

To fight counterfeits and to protect the consumer, the interest in certifying the origin of agricultural goods has been steadily growing in the last years. While numerous works focus on the finished product, an aspect often overlooked is the origin of the raw materials and the direct correlation between chemicals in the soil and the plants. With inductively coupled plasma mass spectrometry (ICP-MS) analysis, trace and ultratrace elements in Sangiovese grapes (the main component of Chianti wine) were measured and their levels were used to investigate the geographical origin of the samples. This was achieved despite the extreme closeness of some of the vineyard partners of this study (10-20 km range) by computing a multivariate model using selected elements as levels. The model was then validated on samples coming from different zones of the Chianti area, with good results for discriminating even extremely close regions.

Metabolic Profiling and Stable Isotope Analysis of Wines: Pilot Study for Cross-Border Authentication

Globalization and free market dynamics have significantly impacted state economies, particularly in the wine industry. These forces have introduced greater diversity in wine products but have also heightened the risk of food fraud, especially in high-value commodities like wine. Due to its market value and the premium placed on quality, wine is frequently subject to adulteration. This issue is often addressed through regulatory trademarks on wine labels, such as Protected Designation of Origin (PDO) and Protected Geographic Indication (PGI). In this context, the metabolic profiles (organic acids, carbohydrates, and phenols) and stable isotope signatures (δ13C, δ18O, D/HI, and D/HII) of red and white wines from four agroclimatically similar regions were examined. The study explored how factors such as grape variety, harvest year, and geographical origin affect wine composition, with a particular focus on distinguishing samples from cross-border areas. Multivariate statistical analysis was used to assess the variability in wine composition and to identify distinct groups of samples. Preliminary results revealed that organic acids and volatile compounds were found in lower concentrations than carbohydrates but were significantly higher than phenols, with levels ranging between 1617 mg/L and 6258 mg/L. Carbohydrate content in the wines varied from 8285 mg/L to 14662 mg/L. Principal Component Analysis (PCA) indicated certain separation trends based on the variance in carbohydrates (e.g., fructose, glucose, galactose) and isotopic composition. However, Discriminant Analysis (DA) provided clear distinctions based on harvest year, variety, and geographical origin.

Carbon stable isotopic compositions of glucose and ethanol in sake after simultaneous saccharification and fermentation processes

The carbon stable isotopic composition of glucose is transferred to that of ethanol during the simultaneous saccharification and fermentation processes of sake production. However, there is limited information regarding carbon isotope discrimination between the ingredient rice and the sake components. Our fermentation experiments show that the carbon stable isotopic composition of rice is intermediate between those of glucose and ethanol in sake and do not differ significantly from those of rice koji and sake lees. Carbon isotope discrimination from rice to ethanol and from glucose to ethanol is 0.9 ± 0.1‰ (mean ± standard deviation, n = 18) and 1.9 ± 0.2‰, respectively. This is approximately half of the isotope discrimination observed in grape wine due to the saccharification process during sake manufacture. Carbon isotope discrimination from ingredient rice to sake components provides valuable insights for the manufacturing process and the authentication of sake.

Application of stable isotopic and mineral elemental fingerprints in identifying the geographical originof concentrated apple juice in China

Food traceability is an important component of food safety and quality. Currently, there is no authentic established technique to identify the origin of concentrated apple juice (CAJ) in China. In this study, the isotopes of δ¹³C, δ¹⁸O and the contents of 32 elements in CAJ from five production areas (BHB, NWR, SCH, LP and YRAR) were determined. The δ¹³C, δ¹⁸O and 28 elements were significantly different (P < 0.05: post-hoc Duncan's test) in the five production areas. PCA, PLS-DA and OPLS-DA were employed for regional classification of samples. The results show that ten key variables (Tl, Se, δ¹⁸O, B, Mg, Sr, Nd, Mo, As, and Na) are more relevant for discrimination of the samples. These findings contribute to understanding the variations of stable isotopic and element compositions in Chinese CAJ depending on geographic origins and offer valuable insight into the control of fraudulent labeling regarding the geographic origins of CAJ.

Assessment of Wine Adulteration Using Near Infrared Spectroscopy and Laser Backscattering Imaging

Food adulteration is in the focus of research due to its negative effect on safety and nutritional value and because of the demand for the protection of brands and regional origins. Portugieser and Sauvignon Blanc wines were selected for experiments. Samples were made by water dilution, the addition of sugar and then a combination of both. Near infrared (NIR) spectra were acquired in the range of 900–1700 nm. Partial least squares regression was performed to predict the adulteration level. The model including all wines and adulterations achieved a prediction error of 0.59% added sugar and 6.85% water dilution. Low-power laser modules were used to collect diffuse reflectance signals at wavelengths of 532, 635, 780, 808, 850, 1064 nm. The general linear model resulted in a higher prediction error of 3.06% added sugar and 20.39% water dilution. Instead of classification, the present study investigated the feasibility of non-destructive methods in the prediction of adulteration level. Laser scattering successfully detected the added sugar with linear discriminant analysis (LDA), but its prediction accuracy was low. NIR spectroscopy might be suitable for rapid non-destructive estimation of wine adulteration.

Identification of Tentative Traceability Markers with Direct Implications in Polyphenol Fingerprinting of Red Wines: Application of LC-MS and Chemometrics Methods

This study investigated the potential of using the changes in polyphenol composition of red wine to enable a more comprehensive chemometric differentiation and suitable identification of authentication markers. Based on high performance liquid chromatography-mass spectrometry (HPLC-MS) data collected from Feteasca Neagra, Merlot, and Cabernet Sauvignon finished wines, phenolic profiles of relevant classes were investigated immediately after vinification (Stage 1), after three months (Stage 2) and six months (Stage 3) of storage, respectively. The data were subjected to multivariate analysis, and resulted in an initial vintage differentiation by principal component analysis (PCA), and variety grouping by canonical discriminant analysis (CDA). Based on polyphenol common biosynthesis route and on the PCA correlation matrix, additional descriptors were investigated. We observed that the inclusion of specific compositional ratios into the data matrix allowed for improved sample differentiation. We obtained simultaneous discrimination according to the considered oenological factors (variety, vintage, and geographical origin) as well as the respective clustering applied during the storage period. Subsequently, further discriminatory investigations to assign wine samples to their corresponding classes relied on partial least squares-discriminant analysis (PLS-DA); the classification models confirmed the clustering initially obtained by PCA. The benefits of the presented fingerprinting approach might justify its selection and warrant its potential as an applicable tool with improved authentication capabilities in red wines.

Differentiation Between Argentine and Austrian Red and White Wines Based on Isotopic and Multi-Elemental Composition

In this work, the characterization of white and red wines from Austria and Argentina was carried out based on the isotopic and multi-elemental profile data. They were determined using vanguard techniques such as isotope ratio mass spectrometry and inductively coupled plasma mass spectrometry. In particular, Al, As, B, Ca, Co, Cu, Fe, K, Li, Mg, Mn, Na, Ni, Pb, Rb, Sr, V, Zn, δ18O, and δ13C were determined. The results show that the samples of wines from Argentina generally present higher concentrations of the elements analyzed compared to Austrian wines. δ18O values from wine water were characteristic of each country, while δ13C values from ethanol did not present any geographical distinction. Linear discriminant analysis using isotopes and elements allowed us to classify 100% of the wines according to the origin and additionally, 98.4% when separately investigating red and white wines. The elements Sr, Li, V, Pb, B, Mn, Co, Rb, As, Na, Mg, Zn, and δ18O were identified as sensitive indicators capable of differentiate wines according to their production origin. Furthermore, Sr, Li, Na, δ13C, δ18O, Ca, B, Fe, Mn, V, Mg, Co, and Zn contributed to the differentiation of wines according to origin and color. To our knowledge, it is the first work that involves the measurement of a wide range of elements and stable isotopes in white and red wines in Argentina, as well as in Austria. This research highlights the power of the application of stable isotopes and multi-element data in multivariate statistical analysis, in order to obtain an accurate differentiation of wines origin.

What's in a wine? - A spot check of the integrity of European wine sold in China based on anthocyanin composition, stable isotope and glycerol impurity analysis

The international wine market has been repeatedly hit by cases of fraud in recent decades. While several studies attested a special vulnerability of the fast growing wine business in China, reports on chemical analyses of commercial wine samples are rare. We examined 50 predominantly red wines with European labelling, which were purchased on the Chinese market, for fraud-relevant parameters. More than 20% of the tested samples revealed anomalies in relation to the stable isotope ratios of D/H, ¹⁸O/¹⁶O and ¹³C/¹²C, contents of technical glycerol by-products or anthocyanin composition. These results strongly suggested watering of the wines, chaptalisation, glycerol addition or the use of non-Vitis anthocyanin sources, respectively. Some of these samples also showed suspicious spelling errors or other irregularities in the labelling, but the majority appeared genuine to the eye. Hence, this spot check demonstrates the importance of chemical authenticity analysis of market samples in order to detect fraudulent products. Moreover, we used the same sample set for an evaluation of the Chinese standard method for carbon stable isotope determination of wine ethanol in comparison to the current OIV (International Organisation of Vine and Wine) standard method. The results of a Bland-Altman analysis indicated that the methods can be applied interchangeably. As the two methods differ in their workflow and in the requested equipment, this might eventually enable more laboratories to perform ¹³C/¹²C analysis of wine and spirits.

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.

Geographical Discrimination of Croatian Wines by Stable Isotope Ratios and Multielemental Composition Analysis

The δ¹⁸O and δ¹³C (analyzed by isotope ratio mass spectrometry, IRMS) and concentration of 22 selected elements (analyzed by inductively coupled plasma—optical emission spectrometry, ICP-OES) in 190 Croatian microvinified and commercial wine samples from continental and coastal winegrowing areas and from three viticultural zones (B, CI, and CII) were measured to investigate whether multivariate statistical methods could provide the fingerprint for geographical origin determination. The highest power for discrimination of wines produced in Croatian winegrowing areas was achieved by general discriminant analysis (GDA) showing correct classification of 97.9% of all investigated samples, 100.0% of microvinified samples and 84.8% of commercial samples in the cross-validation matrix. The most significant markers for discrimination of coastal and continental areas found by GDA were δ¹⁸O and Co, followed by K, Rb, Sn, Li, and δ¹³C in descending order. GDA showed higher levels of correctly classified samples from three viticultural zones in Croatia if only microvinified samples were employed in the analysis (94.9%) than for all samples together (86.3%) or for commercial samples (66.1%) in the cross-validation matrix. The discrimination of viticultural zones B, CI, and CII in Croatia was achieved by δ¹⁸O, Co, Rb, Li, K, and Sn. The results obtained showed that the relationships between the isotopic ratios and concentrations of different considered elements combined with appropriate statistical model represent a powerful tool in discrimination of wines produced in different Croatian winegrowing areas.

Polyphenols: Natural Antioxidants to Be Used as a Quality Tool in Wine Authenticity

Polyphenols are a diverse group of compounds possessing various health-promoting properties that are of utmost importance for many wine sensory attributes. Apart from genetic and environmental parameters, the implementation of specific oenological practices as well as the subsequent storage conditions deeply affect the content and nature of the polyphenols present in wine. However, polyphenols are effectively employed in authenticity studies. Provision of authentic wines to the market has always been a prerequisite meaning that the declarations on the wine label should mirror the composition and provenance of this intriguing product. Nonetheless, multiple cases of intentional or unintentional wine mislabeling have been recorded alarming wine consumers who demand for strict controls safeguarding wine authenticity. The emergence of novel platforms employing instrumentation of exceptional selectivity and sensitivity along with the use of advanced chemometrics such as NMR (nuclear magnetic resonance)- and MS (mass spectrometry)-based metabolomics is considered as a powerful asset towards wine authentication.

Geographical Origin Classification of Chinese Wines Based on Carbon and Oxygen Stable Isotopes and Elemental Profiles

ABSTRACT

Wines from different regions have different qualities due to the impact of geographical location and climate. The sale of inferior wines seriously violates the fair-trade rights of consumers. This article provides an elemental analysis classification method for verifying the geographical origin of wines in the People's Republic of China. Inductively coupled plasma mass spectrometry, liquid chromatography isotope ratio mass spectrometry, and an isotope ratio mass spectrometer were used to analyze 142 wine samples collected from Helan Mountain, Xinjiang, Yunchuanzang, the Yanhuai Valley, and the Hexi Corridor regions. The data included elemental profiles, carbon isotope ratios (δ13C), and oxygen isotope ratios (δ18O). The results of multivariate analysis revealed that the geographical origin of wine is closely related to variations in elemental profiles and isotope ratios. Introducing δ18O and the elements Li, Mn, Ag, In, Th, Ta, and Re into the discriminant model yielded correct classification rates of the linear discriminant model of 90.8% for the training set and 87.3% for the test set.

HIGHLIGHTS

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).

Exploiting combined absorption and front face fluorescence spectroscopy to chase classification: A proof of concept in the case of Sardinian red wines

We present the analysis of optical spectroscopy fingerprints of three different varieties of Sardinian red wine, Carignano, Cannonau and Monica, belonging to Italian DOC (Controlled Origin Designation) red wines family. We selected 15 commercial wines (five for each variety), produced in the middle and south regions of Sardinia form monovarietal grapes. Despite different grapes, farming and tasting, all the samples share common absorption and fluorescence features. A two steps analysis is applied to achieve good classification of the wines. We recorded typical absorbance spectra with a large absorption below 300 nm and a visible absorption band at about 520 nm. Colour attributes according to chromaticity coordinates were evaluated by means of transmittance data and coupled to wine aging data to achieve classification. Despite similar values of lightness (L*), chroma (C*_ab), and hue angle (h°_ab) for the three varieties, good clustering was gathered for Carignano and Cannonau wines, whilst Monica variety has wide spread chromatic coordinates. UV excited fluorescence spectra display large and composite emission bands from the near UV down to the whole visible range. We performed multivariate analysis in the framework of the principal component analysis of fluorescence spectra coupled with absorption ones being able to achieve good clustering between the three varieties. One anomaly among Cannonau wines is discussed.

Upconversion fluorescent nanoparticles based-sensor array for discrimination of the same variety red grape wines

A fluorescent sensor array composed of upconversion nanomaterials to distinguish the same variety of red grape wines was constructed.

Geographical Classification of Tannat Wines Based on Support Vector Machines and Feature Selection

Geographical product recognition has become an issue for researchers and food industries. One way to obtain useful information about the fingerprint of wines is by examining that fingerprint’s chemical components. In this paper, we present a data mining and predictive analysis to classify Brazilian and Uruguayan Tannat wines from the South region using the support vector machine (SVM) classification algorithm with the radial basis kernel function and the F-score feature selection method. A total of 37 Tannat wines differing in geographical origin (9 Brazilian samples and 28 Uruguayan samples) were analyzed. We concluded that given the use of at least one anthocyanin (peon-3-glu) and the radical scavenging activity (DPPH), the Tannat wines can be classified with 94.64% accuracy and 0.90 Matthew’s correlation coefficient (MCC). Furthermore, the combination of SVM and feature selection proved useful for determining the main chemical parameters that discriminate with regard to the origin of Tannat wines and classifying them with a high degree of accuracy. Additionally, to our knowledge, this is the first study to classify the Tannat wine variety in the context of two countries in South America.

Polyphenol Fingerprinting Approaches in Wine Traceability and Authenticity: Assessment and Implications of Red Wines

Like any other food/feed matrix, regardless of the employed analytical method, wine requires authentication strategies; a suitable qualitative and quantitative analysis represents the fingerprint which defines its identity. Until recently, fingerprinting approaches using liquid chromatography applications have been regarded as an effective tool for the assessment of wines employing polyphenol profiles. These profiles are of considerable importance for grapes and wines as they influence greatly the color, sensory, and nutritional quality of the final product. The authenticity and typicity characters are fundamental characteristics, which may be evaluated by the use of polyphenol fingerprinting techniques. Under these conditions, the evolution of polyphenols during the red wine elaboration and maturation processes shows a high importance at the level of the obtained fingerprints. Moreover, the environment factors (vintage, the area of origin, and variety) and the technological conditions significantly influence wine authenticity through the use of polyphenol profiles. Taking into account the complexity of the matter at hand, this review outlines the latest trends in the polyphenol fingerprinting of red wines in association with the transformations that occur during winemaking and storage.

Elemental profile and oxygen isotope ratio (δ18O) for verifying the geographical origin of Chinese wines

The elemental profile and oxygen isotope ratio (δ¹⁸O) of 188 wine samples collected from the Changji, Mile, and Changli regions in China were analyzed by inductively coupled plasma mass spectrometry (ICP-MS), inductively coupled plasma optical emission spectroscopy (ICP-OES) and isotope ratio mass spectrometry (IRMS), respectively. By combining the data of δ¹⁸O and the concentration data of 52 elements, the analysis of variance (ANOVA) technique was firstly applied to obtain the important descriptors for the discrimination of the three geographical origins. Ca, Al, Mg, B, Fe, K, Rb, Mn, Na, P, Co, Ga, As, Sr, and δ¹⁸O were identified as the key explanatory factors. In the second step, the key elements were employed as input variables for the subsequent partial least squares discrimination analysis (PLS-DA) and support vector machine (SVM) analyses. Then, cross validation and random data splitting (training set: test set = 70:30, %) were performed to avoid the over-fitting problem. The average correct classification rates of the PLS-DA and SVM models for the training set were both 98%, while for the test set, these values were 95%, 97%, respectively. Thus, it was suggested that the combination of oxygen isotope ratio (δ¹⁸O) and elemental profile with multi-step multivariate analysis is a promising approach for the verification of the considered three geographical origins of Chinese wines.

Modern analytical methods for the detection of food fraud and adulteration by food category

This review provides current information on the analytical methods used to identify food adulteration in the six most adulterated food categories: animal origin and seafood, oils and fats, beverages, spices and sweet foods (e.g. honey), grain-based food, and others (organic food and dietary supplements). The analytical techniques (both conventional and emerging) used to identify adulteration in these six food categories involve sensory, physicochemical, DNA-based, chromatographic and spectroscopic methods, and have been combined with chemometrics, making these techniques more convenient and effective for the analysis of a broad variety of food products. Despite recent advances, the need remains for suitably sensitive and widely applicable methodologies that encompass all the various aspects of food adulteration. © 2017 Society of Chemical Industry.