Differentiation of Romanian Wines on Geographical Origin and Wine Variety by Elemental Composition and Phenolic Components

Elemental Profile and Health Risk of Fruška Gora Wines

The elemental composition of wine is influenced by endogenous sources and interventions from winemakers. The ICP-MS analysis of Fruška Gora wines (113) from vintages spanning across a decade (2011-2020), produced by 30 wineries and representing 18 autochthonous and international wine varieties, allowed a comprehensive insight into their elemental composition. Based on the mean concentrations of 23 investigated elements, B, Fe, and Mn, which were determined in mg per L of wine regardless of its colour or origin, were the most abundant. Red and white wines showed significant concentration differences in the case of B, Mn, and Sr (higher in red) as well as Be, Al, V, As, Mo, and Pb (higher in white). The elements of the highest toxicological concern were found in all (Pb and As) or almost all of the samples (Cd and Hg). Pb levels (maximum 47.1, 61.6, and 73.2 μg/L in red, rose, and white, respectively) were well below the legal limit. The applied risk assessment approaches (hazard quotient and index, margin of exposure) revealed no health concerns associated with consumption of Fruška Gora wines, except for a slightly increased lifetime cancer risk in the case of high wine consumption, and thus supported the promotion of Fruška Gora wines in the highly competitive international market.

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.

Assessment of Physicochemical, Macro- and Microelements, Heavy Metals, and Related Human Health Risk from Organically, Conventionally, and Homemade Romanian Wines

From the consumers’ perspective, organic and homemade products have become more attractive than conventional ones. However, scientific data regarding the characteristics, properties, and composition of these products are scarce. This study assessed the elemental composition of organic, conventional, and homemade Romanian wines. The physicochemical composition, SO2 (free and total) and total concentration of macroelements, microelements, and heavy metals from nine wine regions containing 165 samples of white (38 organic/70 conventional/57 homemade), 67 red (22/31/14) and 7 rosé (2/2/3) wines were analyzed by inductively coupled plasma mass spectrometry. Dietary intake and target hazard quotient were also evaluated. The phytochemical and elemental compositions of the wine samples varied across regions and wine types. The highest levels of Ca, K, Fe and Al were detected in conventional wines, while homemade wines recorded high concentrations of Na, Mg, V, Ba and Rb. In the case of the rosé wine samples, the levels of trace elements and heavy metals were below the admissible limit. The estimated daily intake of a glass of wine provided less than 0.5% of the tolerable daily intake of the analyzed elements. No health concerns were identified. All wine samples can be safely consumed, regardless of the culture system used for production, and homemade wines are not of a lower quality than organic or conventional wines.

Multi-Sensor Characterization of Sparkling Wines Based on Data Fusion

This paper is focused on the assessment of a multi-sensor approach to improve the overall characterization of sparkling wines (cava wines). Multi-sensor, low-level data fusion can provide more comprehensive and more accurate vision of results compared with the study of simpler data sets from individual techniques. Data from different instrumental platforms were combined in an enriched matrix, integrating information from spectroscopic (UV/Vis and FTIR), chromatographic, and other techniques. Sparkling wines belonging to different classes, which differed in the grape varieties, coupages, and wine-making processes, were analyzed to determine organic acids (e.g., tartaric, lactic, malic, and acetic acids), pH, total acidity, polyphenols, total antioxidant capacity, ethanol, or reducing sugars. The resulting compositional values were treated chemometrically for a more efficient recovery of the underlaying information. In this regard, exploratory methods such as principal component analysis showed that phenolic compounds were dependent on varietal and blending issues while organic acids were more affected by fermentation features. The analysis of the multi-sensor data set provided a more comprehensive description of cavas according to grape classes, blends, and vinification processes. Hierarchical Cluster Analysis (HCA) allowed specific groups of samples to be distinguished, featuring malolactic fermentation and the chardonnay and red grape classes. Partial Least Squares-Discriminant Analysis (PLS-DA) also classified samples according to the type of grape varieties and fermentations. Bar charts and complementary statistic test were performed to better define the differences among the studied samples based on the most significant markers of each cava wine type. As a conclusion, catechin, gallic, gentisic, caftaric, caffeic, malic, and lactic acids were the most remarkable descriptors that contributed to their discrimination based on varietal, blending, and oenological factors.

Characterization of iron walnut in different regions of China based on phytochemical composition

Little is known about the phytochemical composition of iron walnuts. Differences in the geographical origin of iron walnuts associated with economic benefits should also be examined. In this study, the phytochemical composition (fatty acids, Vitamin E, total polyphenols and flavonoids, amino acids, and minerals) of iron walnuts in China was investigated. The results showed that there were significant differences (p < 0.05) in the phytochemical composition of iron walnut oils and flours from different regions. Positive (r > 0.5, p < 0.05) and negative (r < - 0.5, p < 0.05) correlations were found between amino acids/minerals and amino acids/oleic acid, with the highest correlation coefficient (r = 0.742, p < 0.05) between Cu and tyrosine. In addition, based on the 12 phytochemical fingerprints selected by random forest, a geographical-origin identification model for iron walnuts was established, with a corresponding correct classification rate of 96.6%. The top three phytochemical fingerprints for the geographical-origin identification of iron walnut were microelements, macroelements, and antioxidant composition, with contribution rates of 61.7%, 18.1%, and 9.9%, respectively.

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.

Authentication of the geographical origin of Australian Cabernet Sauvignon wines using spectrofluorometric and multi-element analyses with multivariate statistical modelling

With the increased risk of wine fraud, a rapid and simple method for wine authentication has become a necessity for the global wine industry. The use of fluorescence data from an absorbance and transmission excitation-emission matrix (A-TEEM) technique for discrimination of wines according to geographical origin was investigated in comparison to inductively coupled plasma-mass spectrometry (ICP-MS). The two approaches were applied to commercial Cabernet Sauvignon wines from vintage 2015 originating from three wine regions of Australia, along with Bordeaux, France. Extreme gradient boosting discriminant analysis (XGBDA) was examined among other multivariate algorithms for classification of wines. Models were cross-validated and performance was described in terms of sensitivity, specificity, and accuracy. XGBDA classification afforded 100% correct class assignment for all tested regions using the EEM of each sample, and overall 97.7% for ICP-MS. The novel combination of A-TEEM and XGBDA was found to have great potential for accurate authentication of wines.

Pulsed Electric Field Processing of Red Grapes (cv. Rondinella): Modifications of Phenolic Fraction and Effects on Wine Evolution

Pulsed electric field (PEF) is a non-thermal technology able to promote color and polyphenols extraction from grape skins. Most of the publications about PEF in winemaking report data concerning international varieties, poorly considering minor cultivars and the medium/long-term effects of the treatment on wine composition during storage. PEF was applied at different specific energies (2, 10, and 20 kJ kg-1) on grapes of the low-color red cv. Rondinella, after crushing-destemming. Pressing yield, the evolution of color, and total phenolic index (TPI) were measured during skin maceration. Moreover, the wines were characterized for basic compositional parameters, color, anthocyanin profile, phenolic composition (glories indices), metal content (Fe, Cr, and Ni), and sensory characters, two and twelve months after the processing, in comparison with untreated samples and pectolytic enzymes (PE). PEF did not affect fermentation evolution, nor did it modify wine basic composition or metal content. Treatments at 10 and 20 kJ kg-1 led to higher color and TPI in wines, in comparison to PE, because of increased content of anthocyanins and tannins. The sensory evaluation confirmed these findings. Modifications remained stable in wines after twelve months. Glories indices and vitisin A content highlighted greater potential stability of wine color in PEF-treated wines.

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

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

Using neural networks to identify the regional and varietal origin of Cabernet and Merlot dry red wines produced in Krasnodar region

This paper shows a possibility of establishing the authenticity and geographic origin of wines by neural networks based on multi-element analysis. The study used 144 samples of Cabernet and Merlot dry red wines pro- duced in Krasnodar Region according to traditional technologies. The wines were provided by the producers or pur- chased in retail stores. The concentrations of 20 micro- and macroelements in red wines were determined by atomic emission spectroscopy with inductively coupled plasma. The analysis of average elemental contents showed a signi- ficant dependence of wine composition on the grape variety and place of origin, which enabled us to examine inter- relations between the elements and think of a way to identify them by means of classification models. The software STATISTICA Neural Networks was used to assess a possibility of determining the grape variety and geographical origin. The neural networks constructed in the study contained five variables corresponding to the elements with sta- tistically significant correlations between the names of the regions and the wine samples, namely Fe, Mg, Rb, Ti, and Na. These predictors were able to determine the grape variety and place of growth with a sufficiently high accuracy. In the test sample set, the accuracy reached 95.24% and 100% for variety and region identification, respectively. A software product was developed to automate the calculations based on the neural networks. The program can estab- lish the grape variety from a minimal set of microelements, and then, based on the variety and the same set of micro- elements, determine its place of origin.

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).

Characterization of Sparkling Wines According to Polyphenolic Profiles Obtained by HPLC-UV/Vis and Principal Component Analysis

Cava is a sparkling wine obtained by a secondary fermentation in its own bottle. Grape skin contains several compounds, such as polyphenols, which act like natural protectors and provide flavor and color to the wines. In this paper, a previously optimized method based on reversed phase high performance liquid chromatography (HPLC) with ultraviolet/visible (UV/Vis) detection has been applied to determine polyphenols in cava wines. Compounds have been separated in a C₁₈ core-shell column using 0.1% formic acid aqueous solution and methanol as the components of the mobile phase. Chromatograms have been recorded at 280, 310 and 370 nm to gain information on the composition of benzoic acids, hidroxycinnamic acids and flavonoids, respectively. HPLC-UV/vis data consisting of compositional profiles of relevant analytes has been exploited to characterize cava wines produced from different base wine blends using chemometrics. Other oenological variables, such as vintage, aging or malolatic fermentation, have been fixed over all the samples to avoid their influence on the description. Principal component analysis and other statistic methods have been used to extract of the underlying information, providing an excellent discrimination of samples according to grape varieties and coupages.

A contribution to the elemental profile of the leaf samples of newly developed Cabernet Franc varieties

The elemental profile of the leaf samples of three Cabernet Franc clone candidates recently developed in Serbia (Nos. 02, 010 and 012) obtained in the last phase of clonal selection was examined within this study by ICP-OES. Optimal content of Al, Fe, Cu and Zn, the metals well known for their links with a number of neurodegenerative disorders including Alzheimer's disease, has actually highlighted the potential of the leaf sample No. 10 to afford a novel food supplement of natural origin possessing well balanced metal ingredients. Furthermore, lower content of some other elements (Cr, Mn, Ni and Pb) thoroughly supports such a claiming. Finally, the most favourable K/Na ratio observed for the aforementioned sample points out its likely cardioprotectivity. However, two other Cabernet Franc clone candidates might also be recommended for breeding in the same or similar viticultural conditions, since all obtained values were below toxic ones for human consumption.

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.

Profiles of Phenolic Acids and Flavan-3-ols for Select Chinese Red Wines: A Comparison and Differentiation According to Geographic Origin and Grape Variety

The profiles of phenolic acids and flavan-3-ols for the selected Chinese red wines and the potential of using phenolic acids and flavan-3-ols to differentiate the geographic origin and grape variety of wines from China are investigated in this study. Significant differences and markers could be found according to the geographical origin and grape variety. Through a canonical discriminant analysis a good differentiation was developed according to the geographic origin or grape variety, and the accuracy of the discriminant model was 88.9% and 100%, respectively. According to the phenolic acid and flavan-3-ols profiles of the wine samples and good differentiation in the region and the variety discriminant analysis, minimal fraudulent claims were noted for the Chinese red wines investigated.

PRACTICAL APPLICATION

This study provides some help for the protection of geographical origin and monovarietal wine claims.

Classification of red wines using suitable markers coupled with multivariate statistic analysis

Methodologies for chemometric classification of five authentic red wine varieties from Murfatlar wine center, Romania, young and aged are reported. The discriminant analysis based on several anthocyanins, organic acids, (13)C/(12)C, (18)O/(16)O and D/H isotopic ratios, (1)H and (13)C NMR fingerprints revealed a very satisfactory categorization of the wines, both in terms of variety and vintage, thus illustrating the validity of selected variables for wine authentication purposes. LDA applied to the combined data shows 85.7% classification of wines according to grape variety and 71.1% classification of wines according to vintage year, including a control wine set for each categorization, thus allowing an accurate interpretation of the data. Thereby, anthocyanins, certain anthocyanin ratios, oxalic, shikimic, lactic, citric and succinic acids, sugars like glucose, amino acids like histidine, leucine, isoleucine and alanine, and also 2,3-butanediol, methanol, glycerol and isotopic variables were significant for classification of wines.