Origin verification of French red wines using isotope and elemental analyses coupled with chemometrics

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.

Traceability of gushing water in the MiddleRoute of the South-to-North Water Diversion (Beijing section) through the river area

To trace the origin of the gushing water in the riverine area of the Beijing section of The Middle Route of South-to-North Water Diversion Project, a dataset was established comprising water chemistry, three-dimensional fluorescence spectra, and stable isotopes for different water bodies. Results indicated significant differences in Electrical Conductivity (EC), Total Dissolved Solids (TDS), and Ca2+ concentration among the gushing water, river water, and the water from the Middle Route of South-to-North Water Diversion Project (MRSD). Analysis using parallel factor analysis (PARAFAC) and fluorescence index revealed that dissolved organic matter (DOM) in the MRSD mainly originated from endogenous sources, while the river water and gushing water showed influences from both endogenous and exogenous sources. Nitrate sources varied among the water bodies, with distinct contributions from domestic sewage and fertilizer sources. The evaporation lines of river water and gushing water exhibited similar intercepts and slopes, but their intercepts and slopes are much smaller than those of the MRSD, suggesting stronger kinetic evaporative fractionation. In conclusion, the gushing water in the riverine area of the MRSD was determined to originate from the river, providing a fast and efficient method for gushing water source identification.

Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine

To tackle the difficulty of extracting features from one-dimensional spectral signals using traditional spectral analysis, a metabolomics analysis method is proposed to locate two-dimensional correlated spectral feature bands and combine it with deep learning classification for wine origin traceability. Metabolomics analysis was performed on 180 wine samples from 6 different wine regions using UPLC-Q-TOF-MS. Indole, Sulfacetamide, and caffeine were selected as the main differential components. By analyzing the molecular structure of these components and referring to the main functional groups on the infrared spectrum, characteristic band regions with wavelengths in the range of 1000-1400 nm and 1500-1800 nm were selected. Draw two-dimensional correlation spectra (2D-COS) separately, generate synchronous correlation spectra and asynchronous correlation spectra, establish convolutional neural network (CNN) classification models, and achieve the purpose of wine origin traceability. The experimental results demonstrate that combining two segments of two-dimensional characteristic spectra determined by metabolomics screening with convolutional neural networks yields optimal classification results. This validates the effectiveness of using metabolomics screening to determine spectral feature regions in tracing wine origin. This approach effectively removes irrelevant variables while retaining crucial chemical information, enhancing spectral resolution. This integrated approach strengthens the classification model's understanding of samples, significantly increasing accuracy.

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.

Towards Verifying the Imported Soybeans of China Using Stable Isotope and Elemental Analysis Coupled with Chemometrics

Verifying the geographical origin of soybeans (Glycine max [Linn.] Merr.) is a major challenge as there is little available information regarding non-parametric statistical origin approaches for Chinese domestic and imported soybeans. Commercially procured soybean samples from China (n = 33) and soybeans imported from Brazil (n = 90), the United States of America (n = 6), and Argentina (n = 27) were collected to characterize different producing origins using stable isotopes (δ2H, δ18O, δ15N, δ13C, and δ34S), non-metallic element content (% N, % C, and % S), and 23 mineral elements. Chemometric techniques such as principal component analysis (PCA), linear discriminant analysis (LDA), and BP-artificial neural network (BP-ANN) were applied to classify each origin profile. The feasibility of stable isotopes and elemental analysis combined with chemometrics as a discrimination tool to determine the geographical origin of soybeans was evaluated, and origin traceability models were developed. A PCA model indicated that origin discriminant separation was possible between the four soybean origins. Soybean mineral element content was found to be more indicative of origin than stable isotopes or non-metallic element contents. A comparison of two chemometric discriminant models, LDA and BP-ANN, showed both achieved an overall accuracy of 100% for testing and training sets when using a combined isotope and elemental approach. Our findings elucidate the importance of a combined approach in developing a reliable origin labeling method for domestic and imported soybeans in China.

Determination of Metal Content by Inductively Coupled Plasma-Mass Spectrometry in Polish Red and White Wine Samples in Relation to Their Type, Origin, Grape Variety and Health Risk Assessment

The main objective of the research was to assess the influence of selected factors (type of wine, grape variety, origin, alcohol content and daily consumption) on the concentration levels of 26 elements in 53 Polish wine samples, also using chemometric analysis tools. Concentration of Al, As, B, Ba, Be, Cd, Co, Cr, Cu, Fe, Hg, Li, Mn, Ni, Pb, Sb, Se, Sr, Ti, V, Zn and Zr was analyzed by ICP-MS, while concentration of Ca, Na, K and Mg was determined by ICP-OES. White wines were characterized by higher concentrations of Al, As, Be, Ca, Co, Cu, Fe, Hg, Li, Mg, Na, Pb, Sb, Ti, V, Zn and Zr (mean values: 0.075-86,403 μg·L-1 in white wines, 0.069-81,232 μg·L-1 in red wines). Red wines were characterized by higher concentrations of Ba, Cd, Cr, K, Mn, Se and Sr (mean values: 0.407-1,160,000 μg·L-1 in white wines, 0.448-1,521,363 μg·L-1 in red wines). The results obtained for the health risk assessment indices, including the Target Hazard Quotient (THQ, mean values per glass of wine: 2.097 × 10-5 (Cr)-0.041 (B) in all wines), indicate that the analyzed elements do not show a potential toxic effect resulting from wine consumption. The chemometric analysis confirmed that elements such as Li, Ti, Ca, Mn, Sr, Ba, Zn, Mg, Cu, Se and B were closely related to local conditions and soil properties, and the presence of Fe, Cr, V and Pb was related to contamination of the soil.

Differences in Aroma Profile of Cabernet Sauvignon Grapes and Wines from Four Plots in Jieshi Mountain Region of Eastern China

The Bohai Bay region is a famous wine-growing area in China, where the rainfall is concentrated in the summer due to the influence of the temperate semi-humid monsoon climate. As such, the vineyard terrain has a significant impact on the flavor quality of the grapes and the resulting wines. To explore the relationship between the 'Cabernet Sauvignon' wine style and terrain, this study takes four different plots in the Jieshi Mountain region to investigate the differences in the aroma profile of Cabernet Sauvignon grapes and wines of two consecutive vintages. Based on two-way ANOVA, there were 25 free and 8 glycosylated aroma compounds in the grapes and 21 and 10 aroma compounds with an odor activity value greater than 0.1 in the wines at the end of alcohol fermentation (AF) and malolactic fermentation (MLF), respectively, that varied among the four plots. Wines from the four plots showed a significant difference in floral and fruity aroma attributes, which were mainly related to esters with high odor activity values. The difference in concentration of these compounds between plots was more pronounced in 2021 than in 2020, and a similar result was shown on the Shannon-Wiener index, which represents wine aroma diversity. It has been suggested that high rainfall makes the plot effect more pronounced. Pearson's correlation analysis indicated that concentrations of (E)-3-hexen-1-ol in grapes and ethyl 3-methylbutanoate, ethyl hexanoate, isoamyl acetate, isopentanoic acid, and phenethyl acetate in wines were strongly positively correlated with the concentrations of N, P, K, Fe, and electrical conductivity in soil but negatively correlated with soil pH. This study laid a theoretical foundation for further improving the level of vineyard management and grape and wine quality in the Jieshi Mountain region.

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.

Evaluating the Chemical Hazards in Wine Production Associated with Climate Change

The climate warming trend challenges the chemical risk associated with wine production worldwide. The present study investigated the possible difference between chemical wine profile during the drought year 2012 compared to the post-drought year 2013. Toxic metals (Cd and Pb), microelements (Mn, Ni, Zn, Al, Ba, and Cu), macroelements (Na, Mg, K, Ca, and P), isotopic ratios (87Sr/86Sr and 206Pb/207Pb), stable isotopes (δ18O, δ13C, (D/H)I, and (D/H)II), and climatic data were analyzed. The multivariate technique, correlation analysis, factor analysis, partial least squares–discriminant analysis, and hierarchical cluster analysis were used for data interpretation. The maximum temperature had a maximum difference when comparing data year apart. Indeed, extreme droughts were noted in only the spring and early summer of 2012 and in 2013, which increased the mean value of ground frost days. The microelements, macroelements, and Pb presented extreme effects in 2012, emphasizing more variability in terms of the type of wine. Extremely high Cd values were found in the wine samples analyzed, at up to 10.1 µg/L. The relationship between precipitation and δ18O from wine was complex, indicating grape formation under the systematic influence of the current year precipitation, and differences between years were noted. δ13C had disentangled values, with no differentiation between years, and when coupled with the deuterium–hydrogen ratio, it could sustain the hypothesis of possible adulteration. In the current analysis, the 87Sr/86Sr showed higher values than in other Romanian studies. The temperature had a strong positive correlation with Pb, while the ground frost day frequency correlated with both Pb and Cd toxic elements in the wine. Other significant relationships were disclosed between the chemical properties of wine and climate data. The multivariate statistical analysis indicated that heat stress had significant importance in the chemical profile of the wine, and the ground frost exceeded the influence of water stress, especially in Transylvania.

Untargeted metabolomics analysis based on LC-IM-QTOF-MS for discriminating geographical origin and vintage of Chinese red wine

Identifying wine geographical origin and vintage is vital due to the abundance of fraudulent activity associated with wine mislabeling of region and vintage. In this study, an untargeted metabolomic approach based on liquid chromatography/ion mobility quadrupole time-of-flight mass spectrometry (LC-IM-QTOF-MS) was used to discriminate wine geographical origin and vintage. Wines were well discriminated according to region and vintage with orthogonal partial least squares-discriminant analysis (OPLS-DA). The differential metabolites subsequently were screened by OPLS-DA with pairwise modeling. 42 and 48 compounds in positive and negative ionization modes were screened as differential metabolitesfor the discrimination of different wine regions, and 37 and 35 compounds were screened for wine vintage. Furthermore, new OPLS-DA models were performed using these compounds, and the external verification trial showed excellent practicality with an accuracy over 84.2%. This study indicated that LC-IM-QTOF-MS-based untargeted metabolomics was a feasible tool for wine geographical origin and vintage discrimination.

Region, vintage, and grape maturity co-shaped the ionomic signatures of the Cabernet Sauvignon wines

The ionic elements in wine and in vineyards are gaining attention due to characterization of the wine traits, wine origin tracing, and vine nutrient judging. In this experiment, 19 elements were detected by inductively coupled plasma mass spectrometry (ICP-MS) in 69 wine samples from 4 regions, 3 vintages, and 3 grape maturity levels. Furthermore, the elements related to vine development, such as N, P, K, Ca, Mg, Cu, Fe, Zn and Cu in the vineyard soil and petioles were determined. Two orthogonal partial least squares discriminant analysis (O2PLS-DA) showed that K, Mn, Co, Sr, B, Si, Pb, Ni, Cu, and Zn were important elements in distinguishing the regions. High-temperature vintages can bring wines with high levels of Sr in wine. Na, Ca, K, Mg, Rb, Al, Rb, Pb and Fe can be used as signature elements to distinguish wines made from 2 grape maturities. And Cu, Zn, and Mn were the key elements used to differentiate the petioles in the 4 regions. Partial square regression (PLSR) analysis showed that soil pH was positively correlated with Al, B, Ba, K, Pb, Mn, Sr and Rb in wine, and K in wine was significantly positively correlated with element K in the soil. In conclusion, the elemental contents in wine are shaped by the combination of origin, vintage and grape maturity, while some key elements can be used as indicators of origin traceability.

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.

Rapid identification of the geographic origin of Taiping Houkui green tea using near-infrared spectroscopy combined with a variable selection method

BACKGROUND

Most studies focus on the geographically larger production areas in tea traceability. However, famous high-quality tea is often produced in a narrow range of origins, which makes traceability a challenge. In this study, Taiping Houkui (TPHK) green tea of narrow geographical origin was rapidly identified using Fourier-transform near-infrared (FT-NIR) spectroscopy.

RESULTS

First, spectral information of 114 TPHK samples from four production areas was acquired. Second, the synthetic minority over-sampling technique (SMOTE) was used to balance the sample data set, and three different spectral pre-processing methods were compared. Third, three feature variable selection algorithms were used to obtain the pre-processed spectral features. Finally, extreme learning machine (ELM) models based on the variables obtained from the selected features were established to trace the TPHK origin. The optimized ELM model achieves 95.35% classification accuracy in the test set.

CONCLUSION

The present study demonstrates that the optimized variable selection method in combination with NIR spectroscopy represents a suitable strategy for tea traceability in narrow regions. © 2022 Society of Chemical Industry.

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.

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.

Multi-Element Analysis and Origin Discrimination of Panax notoginseng Based on Inductively Coupled Plasma Tandem Mass Spectrometry (ICP-MS/MS)

Panax notoginseng is an important functional health product, and has been used worldwide because of a wide range of pharmacological activities, of which the taproot is the main edible or medicinal part. However, the technologies for origin discrimination still need to be further studied. In this study, an ICP-MS/MS method for the accurate determination of 49 elements was established, whereby the instrumental detection limits (LODs) were between 0.0003 and 7.716 mg/kg, whereas the quantification limits (LOQs) were between 0.0011 and 25.7202 mg/kg, recovery of the method was in the range of 85.82% to 104.98%, and the relative standard deviations (RSDs) were lower than 10%. Based on the content of multi-element in P. notoginseng (total of 89 mixed samples), the discriminant models of origins and cultivation models were accurately determined by the neural networks (prediction accuracy was 0.9259 and area under ROC curve was 0.9750) and the support vector machine algorithm (both 1.0000), respectively. The discriminant models established in this study could be used to support transparency and traceability of supply chains of P. notoginseng and thus avoid the fraud of geographic identification.

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.

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.