Verification of imported red wine origin into China using multi isotope and elemental analyses

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.

Use of energy dispersive X-ray fluorescence to authenticate European wines with protected designation of origin. Challenges of a successful control system based on modelling

Consumers are willing to pay a higher price for food with geographical origin labels such as Protected Designation of Origin and Protected Geographical Indication. In this work, the elemental profile of wine obtained by XRF, combined with multivariate analyses, is used to authenticate 111 Croatian, Italian and Spanish red and white wines, 102 of them from 20 Protected Designations of Origin, reproducing the circumstances faced by control laboratories, using commercially available wines without traceability records. Wines that shared origin clustered together and separated from those of other regions following multivariate statistical tests. Classifications made using Soft Independent Modelling by Class Analogy were characterised by poor sensitivity and specificity. An alternative approach based on successive Partial Least Square Discriminant Analyses with consecutive classifications at country, region and finally, Protected Designation of Origin level, was developed and implemented with good accuracy results. In total, 88 % of the samples were correctly classified.

Identifying the vintage of French wine using stable isotopes, elemental fingerprints, and a data-driven but explainable approach

Stable isotopes and elemental fingerprints were employed as indicators to evaluate the vintage of French wine using climate factors and data-driven models. δ13C of wine ethanol and glycine, and δ18O of wine water and 16 elements were determined in wine from Bordeaux, Burgundy, and Languedoc-Roussillon. Results revealed that isotopic and elemental signatures from various vintages were influenced by precipitation and temperature. If there was less precipitation and higher temperatures during the grape ripening phase, isotopic and elemental signatures had a positive impact on the grapes, resulting in superior quality French wine. Data-driven models achieved excellent accuracy to identify vintages with the identification accuracy up to 72.0 %, and even higher accuracy (up to 95.0 %) from the Bordeaux region. Explainable methods were employed to select the top-5 and top-10 variables for each region under different data-driven models, yielding results comparable to those from a full set of variables.

Variation and fractionation of δ2H, δ18O, and δ17O stable isotopes from irrigation water to soil, grapes, and wine for the traceability of geographical origins

To investigate the variation and fractionation of stable isotopes from irrigation water to soil, grapes, and wine, δ2H, δ18O, and δ17O in different samples from 10 regions in China were determined using a water isotope analyser. The values were significantly different among regions according to the chemometric analysis. All isotopes were significantly and positively correlated with irrigation water-soil and grape-wine. A significant water isotopic fractionation effect was observed from the irrigation water to the soil, grapes, and wine. Stable isotope distribution characteristics correlated with longitude, latitude, altitude, temperature, precipitation, station pressure and wind speed. The linear discriminant analysis (LDA), random forest (RF), support vector machine (SVM), and feed-forward neural network (FNN) models 58.33-100 %, 80-100 %, 53.33-100 %, and 73.33-100 % accurate for distinguishing the geographical origins of all samples from training and test data, respectively. These findings provide a theoretical basis for authenticating the geographic origin of Chinese wines using stable isotope analysis.

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.

Preliminary study on the geographical origin of Chinese 'Cuiguan' pears using integrated stable isotope and multi-element analyses

Distinguish the geographical origin of the pear is important due to the increasingly valued brand protection and reducing the potential food safety risks. In this study, the profiles of stable isotopes (δ13C, δ15N, δ2H, δ18O) and the contents of 16 elements in pear peer from four production areas were analyzed. The δ13C, δ15N, δ2H, δ18O and 12 elements were significantly different (p < 0.05) in the four production areas. Chemometrics analysis including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and linear discriminant analysis (LDA) were exploited for geographical origin classification of samples. OPLS-DA analysis showed that crucial variables (δ13C, δ18O, δ2H, Ni, Cd, Ca, δ15N, Sr and Ga) are more relevant for the discrimination of the samples. OPLS-DA achieved pear origin accuracy rates of 87.76 % by combining stable isotope ratios and elemental contents. LDA had a higher accuracy rate than OPLS-DA, and the LDA analysis showed that the original discrimination rate reached to 100 %, while the cross-validated rate reached to 95.7 %. These studies indicated that this method could be used to assess the geographical discrimination of pear from different producing areas and could potentially control the fair trade of pear in fruit markets.

Signature of Sr isotope ratios and the contents of elements as a tool to distinguish wine regions in China

This study investigated 120 Chinese wines from seven regions and had two objectives: to clarify the Sr isotope ratios and elemental characteristics of each region and to develop a strategy to distinguish the geographic origin of wine without authentic samples to predict its origin. The analyzed 87Sr/86Sr values ranged from 0.708256 to 0.715148, which correlated with the geological characteristics of the regions where they were grown. The Hexi Corridor exhibited the highest ratios of Sr isotopes, while Xinjiang had the lowest. The 87Sr/86Sr values were applied to establish a prediction map which was evaluated through cross-validation. The prediction error was found to be less than 0.00074. The Sr isotope ratio could remain stable for an extended period in a specific location. This map shows the feasibility of identifying wine origin and could be applied to other food products. Adding Sr isotope ratios could improve the accuracy in tracing wine origin.

Authentication of selected white wines by geographical origin using ICP spectrometric and chemometric analysis

An important aspect of assessing the authenticity of wines is its geographical origin. The aim of the work is to authenticate by geographical origin according to the data of the ICP-spectrometric and chemometric analysis of elemental "images" of wines produced from white grape varieties Chardonnay, Riesling and Muscat grown in four regions of the Krasnodar Territory, Russia. The difference in the contents of Al, Ba, Ca and Rb in wines was found depending on the variety, and Al, Ba, Rb, Fe, Li, Sr - depending on the region of grape growth. Different models of the experimental data processing were used for attribution of the produced varieties of wine to the area of the grape's growth. The criterion for the quality of the constructed models was the accuracy of the attribution of a wine variety to the area of the grape's growth (%). Analysis of the elemental analysis data of 153 wine samples showed that in terms of attribution accuracy, automated neural networks (100 %) are preferred among machine learning methods, followed by support vector machines (98.69 %) and general discriminant analysis (94.77 %). The applied mathematical models enabled the revealing of the cluster structure of the analyzed wine varieties and their attribution to the area of a grape growth with high accuracy. Sr, Li and Fe concentrations in wines were found as the dominating predictors in the constructed models for definition of the geographical origin of wines. The combination of ICP-spectrometric analysis data with the capabilities of statistical modeling of machine learning methods focused on large-dimensional data made it possible to successfully solve small-dimensional problems of the definition of the geographical origin of wines by their elemental composition and variety.

Effects of geographical origin, vintage, and soil on stable isotopes and mineral elements in Ecolly grape berries for traceability

Stable isotopes and multi-element profiles of grapes and corresponding soils from different origins and vintages were determined by IRMS and ICP-MS, respectively. Stable isotope ratios and multi-element contents show significant differences among distinct regions and vintages. Grapes and soils were separated using δ2H and δ18O according to regions and vintages. PCA and CA results further verified that multi-element profiles were influenced by origins and vintages. In particular, δ2H, δ18O, and 21 elements in grapes were correlated with those in soil. Redundancy and Spearman analyses revealed that the BCF values were related to the longitude, latitude, altitude, precipitation, and average temperature. RF shows better performance than PLS-DA for discriminating grape origins and vintages. K, Tb, Cs, δ2H, and Co were important variables in discriminating grape origins. These findings confirm that isotopic and elemental profiles depend on the origin, vintage, and soil, establishing a promising method to discriminate grape origins and vintages.

Towards generalizable food source identification: An explainable deep learning approach to rice authentication employing stable isotope and elemental marker analysis

In addressing the generalization issue faced by data-driven methods in food origin traceability, especially when encountering diverse input variable sets, such as elemental contents (C, N, S), stable isotopes (C, N, S, H and O) and 43 elements measured under varying laboratory conditions. We introduce an innovative, versatile deep learning-based framework incorporating explainable analysis, adept at determining feature importance through learned neuron weights. Our proposed framework, validated using three rice sample batches from four Asian countries, totaling 354 instances, exhibited exceptional identification accuracy of up to 97%, surpassing traditional reference methods like decision tree and support vector machine. The adaptable methodological system accommodates various combinations of traceability indicators, facilitating seamless replication and extensive applicability. This groundbreaking solution effectively tackles generalization challenges arising from disparate variable sets across distinct data batches, paving the way for enhanced food origin traceability in real-world applications.

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.

Hydrogen and Oxygen Isotope Fractionation Effects in Different Organ Tissues of Grapes under Drought Conditions

A study of different grapevine tissues and organs (root, stem, leaf, fruit) water isotope fractionation models from high-quality wine grapes produced in the Helan Mountains, a key wine-producing area in northwestern China, was undertaken. Results showed that δ2H values of local groundwater sources were more negative than rivers and precipitation. Soil water δ2H and δ18O values were significantly higher than those of other environmental water sources. Water from the soil surface layer (0-30 cm, δ2H and δ18O values) was more positive than the deeper layer (30-60 cm), indicating that soil water has undergone a positive fractionation effect. δ2H and δ18O values of tissues and organs from different grape varieties followed a similar pattern but were more negative than the local atmospheric precipitation line (slope between 4.1 to 5.2). The 2H and 18O fractionation relationship in grapevine organs was similar, and 18O has a higher fractionation effect than 2H. δ2H and δ18O values showed a strong fractionation effect during the transportation of water to different grape organs (trend of stem > fruit > leaf). This study showed that 18/16O fractionation in grapes is more likely to occur under drought conditions and provides a theoretical basis to improve traceability accuracy and origin protection of wine production areas.

Identification of Oak-Barrel and Stainless Steel Tanks with Oak Chips Aged Wines in Ningxia Based on Three-Dimensional Fluorescence Spectroscopy Combined with Chemometrics

With the increased incidence of wine fraud, a fast and reliable method for wine certification has become a necessary prerequisite for the vigorous development of the global wine industry. In this study, a classification strategy based on three-dimensional fluorescence spectroscopy combined with chemometrics was proposed for oak-barrel and stainless steel tanks with oak chips aged wines. Principal component analysis (PCA), partial least squares analysis (PLS-DA), and Fisher discriminant analysis (FDA) were used to distinguish and evaluate the data matrix of the three-dimensional fluorescence spectra of wines. The results showed that FDA was superior to PCA and PLS-DA in classifying oak-barrel and stainless steel tanks with oak chips aged wines. As a general conclusion, three-dimensional fluorescence spectroscopy can provide valuable fingerprint information for the identification of oak-barrel and stainless steel tanks with oak chips aged wines, while the study will provide some theoretical references and standards for the quality control and quality assessment of oak-barrel aged wines.

Could Collected Chemical Parameters Be Utilized to Build Soft Sensors Capable of Predicting the Provenance, Vintages, and Price Points of New Zealand Pinot Noir Wines Simultaneously?

Soft sensors work as predictive frameworks encapsulating a set of easy-to-collect input data and a machine learning method (ML) to predict highly related variables that are difficult to measure. The machine learning method could provide a prediction of complex unknown relations between the input data and desired output parameters. Recently, soft sensors have been applicable in predicting the prices and vintages of New Zealand Pinot noir wines based on chemical parameters. However, the previous sample size did not adequately represent the diversity of provenances, vintages, and price points across commercially available New Zealand Pinot noir wines. Consequently, a representative sample of 39 commercially available New Zealand Pinot noir wines from diverse provenances, vintages, and price points were selected. Literature has shown that wine phenolic compounds strongly correlated with wine provenances, vintages and price points, which could be used as input data for developing soft sensors. Due to the significance of these phenolic compounds, chemical parameters, including phenolic compounds and pH, were collected using UV-Vis visible spectrophotometry and a pH meter. The soft sensor utilising Naive Bayes (belongs to ML) was designed to predict Pinot noir wines' provenances (regions of origin) based on six chemical parameters with the prediction accuracy of over 75%. Soft sensors based on decision trees (within ML) could predict Pinot noir wines' vintages and price points with prediction accuracies of over 75% based on six chemical parameters. These predictions were based on the same collected six chemical parameters as aforementioned.

Simultaneously Verifying the Original Region of Green and Roasted Coffee Beans by Stable Isotopes and Elements Combined with Random Forest

Simultaneously verifying the original region of green and roasted coffee beans is very important for protecting legal interests of the stakeholder according to the chemical analyzing method. 131 green coffee bean samples are collected from six different original regions and pretreated with three degrees (green, middle, and dark roasted); five stable isotope ratios (δ13C, δ14N, δ18O, δ2H, and δ32S) and twelve elemental contents (Al, Cr, Ni, Zn, Ba, Cu, Na, Mn, Fe, Ca, K, and Mg) of green, middle, and dark roasted coffee bean samples (131×3) were analyzed. Fractionation of stable isotopes and variation of elemental contents were evaluated, only isotope hydrogen (2H) significantly fractionated, and elemental concentrations increased with a certain rate during the roasting process. One-way analysis of variance (ANOVA) was used to compare the stable isotope ratios and elemental concentrations of all coffee bean samples from six different original regions. Random forest (RF) was employed to build a discriminating model for simultaneously verifying the original regions of green and roasted coffee bean samples; this model provided 100% accuracy. Inclusion of this mathematical model for simultaneously verifying the original region of green and roasted coffee beans had powerful distinguishing capability and which will not be influenced by fractionation of hydrogen (2H) and variation of element contents during the roasted process.

Geographical origin traceability of Sengcu rice using elemental markers and multivariate analysis

Multi-element analysis combined with chemometric method has been used to investigate the distinguish between Sengcu rice and other types of rice origins in Vietnam. In Sengcu rice, As, Ba Sr, Pb, Ca, Se were confirmed as the key elements for geographical traceability among three fields of Lao Cai, whereas Al, Ca, Fe, Mg, Ag, As were major factors to distinguish between Sengcu and other types of rice. Based on linear discriminant analysis and partial least squares-discriminant analysis model, overall correct identification rates distinguishing between Sengcu and other types of rice were approximately 100% in both training and validation test. Moreover, to distinguish geographical origin of Sengcu rice samples, these rates vary from 80% to 99%. These results suggest the presence of food adulteration illustrated in the latter.

Untargeted metabolomic analysis of Chinese red wines for geographical origin traceability by UPLC-QTOF-MS coupled with chemometrics

Identifying geographical origins of red wines made in specific regions is of significance since the false claim of geographical origins has been frequently exposed in China's wine industry. In this work, an untargeted metabolomic approach based on UPLC-QTOF-MS was established to discriminate geographical origins of Chinese red wines. The principal component analysis (PCA) showed significant differences between wine samples from three famous geographical origins in China. The metabolites contributing to the differentiation were screened by orthogonal partial least squares-discriminant analysis (OPLS-DA) with pairwise modeling. 40 and 46 differential metabolites in positive and negative ionization modes were putatively identified as chemical markers. Furthermore, heatmap visualization and OPLS-DA models were constructed based on these identified markers and external verification wine samples from different regions were successfully discriminated, with recognition rate up to 96.7%. This study indicated that UPLC-QTOF-MS-based untargeted metabolomics has great potential for the geographical origin traceability of Chinese red wines.

Determining the Geographical Origin of Fuji Apple from China by Multivariate Analysis Based on Soluble Sugars, Organic Acids, and Stable Isotopes

The aim of this study was to explore the regional characteristics of soluble sugars, organic acids, and stable isotopes (δ2H, δ18O, and δ13C) in Fuji apple and the viability of tracing the geographical origin. Totally, 181 Fuji apple samples from 2017 and 2018 from three main apple production regions in China, Bohai Bay (BHB), Loess Plateau (LP), and Northwest region (NW) were collected. The parameters of soluble sugars, organic acids, and stable isotopes in samples were analyzed with HPLC, IC, and IRMS, respectively. The results of regional difference analysis, multiway variance analysis, and correlation analysis indicated that sorbitol (Sor), glucose (Glu), fructose (Fru), sucrose (Sucr), δ2H, and δ13C can be used to distinguish the samples from the three regions. Stepwise linear discriminant analysis (SLDA) showed that the correct discriminant rate of samples from the advantageous production areas of apples in China (BHB and LP) was 82.2%, and the most effective indexes were Glu, Fru, Sucr, and δ2H. Moreover, satisfactory classification can be achieved in samples from BHB and NW, with a correct classification rate of 90.0%, and Sor, Glu, and Fru were included in the discrimination model. Furthermore, the validity of the discriminant model was verified by the prediction set. The study also found that organic acids were not suitable to distinguish the apple samples from the three regions. In addition, soluble sugars and stable isotopes could not effectively distinguish LP and NW samples, which was also the reason that the samples from the three main apple production regions could not be distinguished well.

An overview on multi-elemental profile integrated with chemometrics for food quality assessment: toward new challenges

Food products, especially those with high value-added, are commonly subjected to strict quality controls, which are of paramount importance, especially for attesting to some peculiar features related, for instance, to their geographical origin and/or the know-how of their producers. However, the sophistication of fraudulent practices requires a continuous update of analytical platforms. Different analytical techniques have become extremely appealing since the instrumental analysis tools evolution has substantially improved the capability to reveal and understand the complexity of food. In light of this, multi-elemental composition has been successful implemented solving a plethora of food authentication and traceability issues. In the last decades, it has existed an ever-increasing trend in analysis based on spectrometry analytical platforms in order to obtain a multi-elemental profile that combined with chemometrics have been noteworthy analytical methodologies able to solve these problems. This review provides an overview of published reports in the last decade (from 2011 to 2021) on food authentication and quality control from their multi-element composition in order to evaluate the state-of-the-art of this field and to identify the main characteristics of applied analytical techniques and chemometric data treatments that have permit achieve accurate discrimination/classification models, highlighting the strengths and the weaknesses of these methodologies.

Chemometric approaches for determining the geographical origin of Japanese Chardonnay wines using oxygen stable isotope and multi-element analyses

Determining the geographical origin of wines is a major challenge in wine authentication, but little information is available regarding non-parametric statistical approaches for wines. In this study, we collected 33 domestic Chardonnay wines vinified on a small scale from grapes cultivated in Japan, and 42 Chardonnay wines imported from 8 countries, for oxygen stable isotope and multi-element analyses. Non-metric multidimensional scaling (NMDS), kernel principal component analysis (KPCA) and principal component analysis (PCA) were applied to the oxygen stable isotopic compositions (δ¹⁸O) and the concentrations of 18 elements in the wines to compare the extractions by parametric and non-parametric methods. The non-parametric methods, NMDS and KPCA, separated domestic from imported Chardonnay wines better than the parametric method, PCA. Of 19 variables, 18 were important for geographical discrimination, with the δ¹⁸O value being the most significant in all statistic methods. Non-parametric multivariate analyses will help discriminate domestic from imported Chardonnay wines.

Analysis of Selected Minerals in Homemade Grape Vinegars Obtained by Spontaneous Fermentation

Fruit vinegars are widely used as a spice and food preservative. They are considered as functional food, containing many bioactive compounds with pro-health benefits. Grape vinegars can be also a source of mineral compounds. Their quantity and diversity can be determined by environmental factors and growing conditions, such as temperature, mineral composition of the soil, heavy metal contamination, sunlight availability as well as grape variety and fruit ripeness stage. The aim of the study was to determine the content of minerals in homemade grape vinegars, obtained by spontaneous fermentation. Five different grape (Vitis vinifera L.) varieties were used in the study (Cabernet Cortis, Johanniter, Solaris, Souvignier gris and Prior). Moreover, the effect of sugar addition in the fermentation process on the mineral content was examined. The mineral content was determined using the ICP-OES method. Among the analysed samples, potassium was the most abundant element (936.07-1472.3 mg/L of vinegar). Comparative analysis showed that the content of Ca, Fe and Cr was significantly higher in vinegars prepared from red varieties than in white-coloured ones. In turn, vinegars prepared from white grape varieties contained statistically significantly higher content of potassium. Vinegar colour did not have a significant influence on the content of the remaining elements included in the analysis. Furthermore, statistical analysis did not reveal any significant differences in the content of the analysed minerals in any of the grape varieties used between the samples with and without sugar addition.

From Soil to Grape and Wine: Geographical Variations in Elemental Profiles in Different Chinese Regions

Elemental profiles are frequently applied to identify the geographical origin and authenticity of food products, to guarantee quality. The concentrations of fifteen major, minor, and trace elements (Na, Mg, K, Ca, Al, Fe, Mn, Cu, Zn, Rb, Sr, Li, Cd, Cs, and Ba) were determined in soils, “Meili” grapes, and wines from six regions in China by inductively coupled plasma mass spectrometry (ICP-MS). The elemental concentrations in these samples, according to the geographical origins, were analyzed by one-way analysis of variance (ANOVA) with Duncan’s multiple comparisons. The bioconcentration factor (BCF) from soil to grape and the transfer factor (TF) from grape to wine were calculated. Mg, K, Ca, Cu, Zn, Rb, Sr, and Ba presented higher BCF values than the other seven elements. The TF values of six elements (Na, Mg, K, Zn, Li, and Cs) were found to be greater than one. Moreover, the correlation of element content between the pairs of soil–grape, grape–wine, and bioconcentration factor (BCF)–environmental factor were analyzed. Significant correspondences among soil, grape, and wine were observed for K and Li. Two elements (Sr and Li) showed significant correlations between BCF and environmental factor (relative humidity, temperature, and latitude). A linear discriminant analysis (LDA) with three variables (K, Sr, Li) revealed a high accuracy (>90%) to determine the geographical origin for different Chinese regions.

Recent techniques for the authentication of the geographical origin of tea leaves from camellia sinensis: A review

Tea is one of the most important beverages worldwide, is produced in several distinct geographical regions, and is traded on the global market. The ability to determine the geographical origin of tea products helps to ensure authenticity and traceability. This paper reviews the recent research on authentication of tea using a combination of instrumental and chemometric methods. To determine the production region of a tea sample, instrumental methods based on analyzing isotope and mineral element contents are suitable because they are less affected by tea variety and processing methods. Chemometric analysis has proven to be a valuable method to identify tea. Principal component analysis (PCA) and linear discriminant analysis (LDA) are the most preferred methods for processing large amounts of data obtained through instrumental component analysis.

Multivariate analysis for organic milk authentication

To differentiate organic milk (OM) from conventional milk (CM), an orthogonal projection to latent structure-discriminant analysis (OPLS-DA) model was constructed using δ¹³C, δ¹⁵N, δ¹⁸O, 51 elements and 35 fatty acids (FAs) as the variables. So far, most reported studies barely use three or more types of variables, but more variables could avoid one-sidedness and get stabler models. Our multivariate model combines geographical and nutritional parameters and displays better explanatory and predictive abilities (R²X = 0.647, R²Y = 0.962 and Q² = 0.821) than models based on fewer variables for differentiating OM and CM. In particular, δ¹⁵N, Se, δ¹³C, Eu, K and α-Linolenic acid (ALA) are found to be critical parameters for the discrimination of OM. These results show that the multivariate model based on stable isotopes, elements and FAs can be used to identify OM, and can potentially expand the global databases for quality and authenticity of milk.

Determination of enantiomeric and stable isotope ratio fingerprints of active secondary metabolites in neroli (Citrus aurantium L.) essential oils for authentication by multidimensional gas chromatography and GC-C/P-IRMS

Neroli essential oil (EO), extracted from bitter orange blossoms, is one of the most expensive natural products on the market due to its poor yield and its use in fragrance compositions, such as cologne. Multiple adulterations of neroli EO are found on the market, and several authentication strategies, such as enantioselective gas chromatography (GC) and isotope ratio mass spectrometry (IRMS), have been developed in the last few years. However, neroli EO adulteration is becoming increasingly sophisticated, and analytical improvements are needed to increase precision. Enantiomeric and compound-specific isotopic profiling of numerous metabolites using multidimensional GC and GC-C/P-IRMS was carried out. These analyses proved to be efficient for geographical tracing, especially to distinguish neroli EO of Egyptian origin. In addition, δ²H values and enantioselective ratios can identify an addition of 10% of petitgrain EO. These results demonstrate that enantioselective and stable isotopic metabolite fingerprint determination is currently a necessity to control EOs.

Validation and analysis of the geographical origin of Angelica sinensis (Oliv.) Diels using multi-element and stable isotopes

Background

Place of origin is an important factor when determining the quality and authenticity of Angelica sinensis for medicinal use. It is important to trace the origin and confirm the regional characteristics of medicinal products for sustainable industrial development. Effectively tracing and confirming the material’s origin may be accomplished by detecting stable isotopes and mineral elements.

Methods

We studied 25 A. sinensis samples collected from three main producing areas (Linxia, Gannan, and Dingxi) in southeastern Gansu Province, China, to better identify its origin. We used inductively coupled plasma mass spectrometry (ICP-MS) and stable isotope ratio mass spectrometry (IRMS) to determine eight mineral elements (K, Mg, Ca, Zn, Cu, Mn, Cr, Al) and three stable isotopes (δ¹³C, δ¹⁵N, δ¹⁸O). Principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were used to verify the validity of its geographical origin.

Results

K, Ca/Al, δ¹³C, δ¹⁵N and δ¹⁸O are important elements to distinguish A. sinensis sampled from Linxia, Gannan and Dingxi. We used an unsupervised PCA model to determine the dimensionality reduction of mineral elements and stable isotopes, which could distinguish the A. sinensis from Linxia. However, it could not easily distinguish A. sinensis sampled from Gannan and Dingxi. The supervised PLS-DA and LDA models could effectively distinguish samples taken from all three regions and perform cross-validation. The cross-validation accuracy of PLS-DA using mineral elements and stable isotopes was 84%, which was higher than LDA using mineral elements and stable isotopes.

Conclusions

The PLS-DA and LDA models provide a theoretical basis for tracing the origin of A. sinensis in three regions (Linxia, Gannan and Dingxi). This is significant for protecting consumers’ health, rights and interests.

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.

Metabolomics as a marketing tool for geographical indication products: a literature review

Geographical indication (GI) is used to identify a product's origin when its characteristics or quality are a result of geographical origin, which includes agricultural products and foodstuff. Metabolomics is an “omics” technique that can support product authentication by providing a chemical fingerprint of a biological system, such as plant and plant-derived products. The main purpose of this article is to verify possible contributions of metabolomic studies to the marketing field, mainly for certified regions, through an integrative review of the literature and maps produced by VOSviewer software. The results indicate that studies based on metabolomics approaches can relate specific food attributes to the region’s terroir and know-how. The evidence of this connection, marketing of GIs and metabolomics methods, is viewed as potential tool for marketing purposes (e.g., to assist communication of positive aspects and quality), and legal protection. In addition, our results provide a taxonomic categorization that can guide future marketing research involving metabolomics. Moreover, the results are also useful to government agencies to improve GIs registration systems and promotion strategies.

Determining the geographical origin of milk by multivariate analysis based on stable isotope ratios, elements and fatty acids

To construct a reliable discrimination model for determining milk geographical origin, stable isotope ratios including δ¹³C, δ¹⁵N and δ¹⁸O, 51 elements and 35 fatty acids (FAs) in milk samples from Australia, New Zealand and Austria were detected and analyzed. It is found that all of the stable isotope ratios in the milk samples of Australia are the highest, followed by those of the samples from New Zealand and Austria. In addition, 14 elements and 8 FAs show different contents in the samples of different countries at the significance level of P < 0.05. Based on these results, a multivariate model with good robustness and predictive ability for authenticating milk origin (R²X = 0.693, Q² = 0.854) was successfully constructed. Element contents and stable isotope ratios are more reliable variables for milk origin discrimination and Rb, δ¹⁸O, Tl, Ba, Mo, Sr, δ¹⁵N, Cs, As, Eu, C20:4n6, Sc, C13:0, K, Ca and C16:1n7 are the critical markers in the multivariate model for verifying milk 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.

Spatial Fingerprinting: Horizontal Fusion of Multi-Dimensional Bio-Tracers as Solution to Global Food Provenance Problems

Building the capacity of efficiently determining the provenance of food products represents a crucial step towards the sustainability of the global food system. Despite species specific empirical examples of multi-tracer approaches to provenance, the precise benefit and efficacy of multi-tracers remains poorly understood. Here we show why, and when, data fusion of bio-tracers is an extremely powerful technique for geographical provenance discrimination. Specifically, we show using extensive simulations how, and under what conditions, geographical relationships between bio-tracers (e.g., spatial covariance) can act like a spatial fingerprint, in many naturally occurring applications likely allowing rapid identification with limited data. To highlight the theory, we outline several statistic methodologies, including artificial intelligence, and apply these methodologies as a proof of concept to a limited data set of 90 individuals of highly mobile Sockeye salmon that originate from 3 different areas. Using 17 measured bio-tracers, we demonstrate that increasing combined bio-tracers results in stronger discriminatory power. We argue such applications likely even work for such highly mobile and critical fisheries as tuna.

Developing an authentication approach using SPME-GC-IRMS based on compound-specific δ 13C analysis of six typical volatiles in wine

An analytical method using gas chromatography isotope ratio mass spectrometry (GC-IRMS) combined with solid phase micro-extraction (SPME) was developed to measure the δ 13C values of six typical volatiles commonly occurring in wine (isoamyl acetate, 2-octanone, limonene, 2-phenylethanol, ethyl octanoate and ethyl decanoate) for the first time. SPME selected with a divinylbenzene/carboxen/polydimethylsiloxane fiber was combined with the GC-IRMS for pretreatment optimization. The optimized SPME parameters of extraction time, extraction temperature and salt concentration were 40 min, 40 °C and 10%, respectively. The δ 13C values measured by SPME-GC-IRMS were in good agreement with those measured via elemental analyzer (EA)-IRMS and GC-IRMS. The differences range from 0.02 to 0.44‰ with EA-IRMS and from 0 to 0.28‰ with GC-IRMS, indicating the high accuracy of the method. This newly established method measured the precision within 0.30‰ and was successfully validated to discriminate imported real wine samples with identical label but amazing price differences from different importers.

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.