The Use of Ultra-Fast Gas Chromatography for Fingerprinting-Based Classification of Zweigelt and Rondo Wines with Regard to Grape Variety and Type of Malolactic Fermentation Combined with Greenness and Practicality Assessment.

In food authentication, it is important to compare different analytical procedures and select the best method. The aim of this study was to determine the fingerprints of Zweigelt and Rondo wines through headspace analysis using ultra-fast gas chromatography (ultra-fast GC) and to compare the effectiveness of this approach at classifying wines based on grape variety and type of malolactic fermentation (MLF) as well as its greenness and practicality with three other chromatographic methods such as headspace solid-phase microextraction/gas chromatography-mass spectrometry with carboxen-polydimethylosiloxane fiber (SPME/GC-MS with CAR/PDMS fiber), headspace solid-phase microextraction/gas chromatography-mass spectrometry with polyacrylate fiber (SPME/GC-MS with PA fiber), and ultra performance liquid chromatography-photodiode array detector-tandem mass spectrometry (UPLC-PDA-MS/MS). Principal Component Analysis (PCA) revealed that fingerprints obtained using all four chromatographic methods were suitable for classification using machine learning (ML). Random Forest (RF) and Support Vector Machines (SVM) yielded accuracies of at least 99% in the varietal classification of Zweigelt and Rondo wines and therefore proved suitable for robust fingerprinting-based Quality Assurance/Quality Control (QA/QC) procedures. In the case of wine classification by the type of MLF, the classifiers performed slightly worse, with the poorest accuracy of 91% for SVM and SPME/GC-MS with CAR/PDMS fiber, and no less than 93% for the other methods. Ultra-fast GC is the greenest and UPLC-PDA-MS/MS is the most practical of the four chromatographic methods.

A Novel Data Fusion Strategy of GC-MS and 1H NMR Spectra for the Identification of Different Vintages of Maotai-flavor Baijiu.

Counterfeit Baijiu has been emerging because of the price variances of real-aged Chinese Baijiu. Accurate identification of different vintages is of great interest. In this study, the combination of gas chromatography-mass spectrometry (GC-MS) and proton nuclear magnetic resonance (1H NMR) spectroscopy was applied for the comprehensive analysis of chemical constituents for Maotai-flavor Baijiu. Furthermore, a novel data fusion strategy combined with machine learning algorithms has been established. The results showed that the midlevel data fusion combined with the random forest algorithm were the best and successfully applied for classification of different Baijiu vintages. A total of 14 differential compounds (belonging to fatty acid ethyl esters, alcohols, organic acids, and aldehydes) were identified, and used for evaluation of commercial Maotai-flavor Baijiu. Our results indicated that both volatiles and nonvolatiles contributed to the vintage differences. This study demonstrated that GC-MS and 1H NMR spectra combined with a data fusion strategy are practical for the classification of different vintages of Maotai-flavor Baijiu.

MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion.

Based on the current research on the wine grape variety recognition task, it has been found that traditional deep learning models relying only on a single feature (e.g., fruit or leaf) for classification can face great challenges, especially when there is a high degree of similarity between varieties. In order to effectively distinguish these similar varieties, this study proposes a multisource information fusion method, which is centered on the SynthDiscrim algorithm, aiming to achieve a more comprehensive and accurate wine grape variety recognition. First, this study optimizes and improves the YOLOV7 model and proposes a novel target detection and recognition model called WineYOLO-RAFusion, which significantly improves the fruit localization precision and recognition compared with YOLOV5, YOLOX, and YOLOV7, which are traditional deep learning models. Secondly, building upon the WineYOLO-RAFusion model, this study incorporated the method of multisource information fusion into the model, ultimately forming the MultiFuseYOLO model. Experiments demonstrated that MultiFuseYOLO significantly outperformed other commonly used models in terms of precision, recall, and F1 score, reaching 0.854, 0.815, and 0.833, respectively. Moreover, the method improved the precision of the hard to distinguish Chardonnay and Sauvignon Blanc varieties, which increased the precision from 0.512 to 0.813 for Chardonnay and from 0.533 to 0.775 for Sauvignon Blanc. In conclusion, the MultiFuseYOLO model offers a reliable and comprehensive solution to the task of wine grape variety identification, especially in terms of distinguishing visually similar varieties and realizing high-precision identifications.

The meaning of the word elegance as a wine descriptor: Effect of expertise and wine type.

The aim of the present study was to investigate the concept of elegance in wine as a function of (i) domain-specific expertise, and (ii) wine type, more specifically as applied to still wine and to sparkling wine. One hundred and fifty participants, classified into five categories depending on their wine-relevant expertise, completed an online questionnaire aimed at inducing verbal responses concerning their cerebral representations of elegance as applied to still wines and sparkling wines. The five participant categories comprised four expert groups, namely wine producer [30], wine writer/critics [30], sommeliers [30], wine merchant/seller [30], and a novice group of wine consumers [30]. Results showed that both professional and novice participants found the concept of 'elegance' applicable to both still wine and sparkling wine, evoking words and terms with reasonable consistency overall. Differences were found, both between participant groups and between wine types. After categorisation, significant differences between participant categories were found for two of the ten categories of words elicited for both inducing expressions. Extrinsic quality and intrinsic quality were significantly different across occupational categories for 'elegant in relation to wine'. Extrinsic quality was noticeably higher for consumers than the other participants while intrinsic quality appears more important to conceptualisations of wine elegance for wine writers/critics than it is for the other groups of participants. Concerning 'elegance in sparkling wine', extrinsic quality also showed significant differences across participant categories, being highly evoked by producers and merchants/sellers. The category context/analogy was significantly higher in consumers' associations and interestingly, lower in writer/critics' associations. These results support the notion that application of the term elegance as a wine descriptor is seen as appropriate by both wine professionals and wine consumers, suggesting that the term elegant when applied to either still or sparkling wine evokes a concept that is not idiosyncratic but that has a central structure shared by many wine professionals and consumers, the core of this structure including terms smooth, balanced, refined and complex.

Geographical origin authentication of southern Brazilian red wines by means of EEM-pH four-way data modelling coupled with one class classification approach.

EEM data recorded at different pH values was exploited by MCR-ALS in order to determine qualitative information about Brazilian red wines. In addition, the geographical traceability of wines produced in the Serra Gaúcha (Rio Grande do Sul) was carried out by DD-SIMCA considering 53 samples from the target class and 20 from other producing regions. The fluorescence signal corresponds to 9 EEMs recorded at different pH (3-11), generating four-way data. By MCR-ALS decomposition, eight factors were retrieved and related to typical chemical compounds found in red wine. In addition, the EEM pH data was used to build a one-class classification model, considering that MCR scores and all samples of the target class were properly recognised as belonging to the target class, with maximal sensitivity equal to 1. Samples of the non-target class were also adequately rejected by the model, and the specificity was found to be 0.97.

Characterization of key odor-active compounds in sweet Petit Manseng (Vitis vinifera L.) wine by gas chromatography-olfactometry, aroma reconstitution, and omission tests.

Petit Manseng (Vitis vinifera L.) has become a popular variety in China for the production of semisweet and sweet wines. However, few studies focused on investigating the molecular odor code of its key odorants. In this study, the key odor-active compounds of Chinese sweet Petit Manseng wine were identified by gas chromatography-olfactometry (GC-O) and gas chromatography-mass spectrometry (GC-MS). Fifty-five odorous zones were sniffed and identified by application of aroma extraction dilution analysis on a distillate prepared by liquid-liquid extraction and solvent-assisted flavor evaporation. Among them, isoamyl alcohol, ethyl octanoate, isovaleric acid, (E)-ß-damascenone, and phenylethanol particularly displayed with highest flavor dilution factors above 1024. The quantification of volatiles by headspace solid-phase microextraction coupled with GC-MS and GC coupled with triple quadrupole MS/MS and a calculation of odor activity values (OAVs) indicated 23 volatiles with OAVs above 1. Ethyl hexanoate showed the highest OAV with 208.8, followed by (E)-ß-damascenone (189.0), 3-mercaptohexanol (60.3), isoamyl acetate (45.4), and furaneol (40.1). The aroma of the sweet wine was successfully reconstituted by combining 42 aroma compounds in a model wine solution. 3-Mercaptohexanol, (E)-ß-damascenone, furaneol, γ-octalactone + γ-decalactone + γ-hexalactone, and ethyl cinnamate had important influence on the aroma of sweet Petit Manseng wine assessed by omission tests. Moreover, the discrimination of wines from three regions was successfully achieved by partial least squares discriminant analysis based on quantitative results of key odorants. 3-Mercaptohexanol was considered as the most responsible for the region discrimination and had highest concentrations in Petit Manseng wines from Jiaodong Peninsula. PRACTICAL APPLICATION: Understanding of the knowledge in key odorants of Petit Manseng wines could be useful to improve wine quality through viticultural and enological practices.

What's in this drink? Classification and adulterant detection in Irish Whiskey samples using near infrared spectroscopy combined with chemometrics.

BACKGROUND: Near-infrared (NIR) spectroscopy coupled with principal component analysis (PCA) and partial least squares (PLS) regression was used to analyse a series of different Irish Whiskey samples in order to define their spectral profile and to assess the capability of the NIR method to identify samples based on their origin and storage (e.g. distiller, method of maturation). The ability of NIR spectroscopy to quantify the level of potential chemical adulterants was also investigated. Samples were spiked with 0.1%, 0.5%, 1.0%, 1.5% and 2.0% v/v of each adulterant (e.g. methanol, ethyl acetate, etc.) prior to NIR analysis. RESULTS: The results of this study demonstrated the capability of NIR spectroscopy combined with PLS regression to classify the whiskey samples and to determine the level of adulteration. Moreover, the potential of NIR coupled with chemometric analysis as a rapid, portable, and non-destructive screening tool for quality control, traceability, and food/beverage adulteration for customs and other regulatory agencies, to mitigate beverage fraud was illustrated. CONCLUSION: Given the non-specificity of the NIR technique, these positive preliminary results indicated that this method of analysis has the potential to be applied to identify the level of adulteration in distilled spirits. The rapid nature of the technique and lack of consumables or sample preparation required allows for a far more time and cost-effective analysis per sample. © 2021 Society of Chemical Industry.

Multi-Elemental Analysis of Wine Samples in Relation to Their Type, Origin, and Grape Variety.

Wine is one of the most popular alcoholic beverages. Therefore, the control of the elemental composition is necessary throughout the entire production process from the grapes to the final product. The content of some elements in wine is very important from the organoleptic and nutritional points of view. Nowadays, wine studies have also been undertaken in order to perform wine categorization and/or to verify the authenticity of products. The main objective of this research was to evaluate the influence of the chosen factors (type of wine, producer, origin) on the levels of 28 elements in 180 wine samples. The concentration of studied elements was determined by ICP-MS (Ag, B, Ba, Be, Bi, Cd, Co, Cr, Cu, Li, Mn, Mo, Ni, Pb, Rb, Sb, Sn, Sr, Te, Tl, U, Zn), ICP-OES (Ca, Fe, K, Mg, Ti), and CVAAS (Hg) techniques in 79 red, 75 white, and 26 rose wine samples. In general, red wines contained higher values of mean and median of B, Ba, Cr, Cu, Mn, Sr and Zn in contrast to other wine types (white and rose). In white wines (when compared to red and rose wines) higher levels of elements such as Ag, Be, Bi, Cd, Co, Li, K and Ti were determined. In contrast, rose wines were characterized by a higher concentration of Fe and U. The study also revealed that in the case of 18 samples, the maximum levels of some metals (Cd-8 samples, Pb-9 samples, Cu-1 sample) were slightly exceeded according to the OIV standards, while for Zn and Ti in any wine sample the measured concentrations of these metals were above the permissible levels. Thus, it can be stated that the studied wines contained, in general, lower levels of heavy metals, suggesting that they should have no effect on the safety of consumption. The results also showed higher pH level for red wines as a consequence of the second fermentation process which is typically carried out for this type of wine (malolactic fermentation). The highest median value of pH was reported for Merlot-based wines, while the lowest was for Riesling. It is assumed that dry Riesling has a higher content of tartaric and malic acid than dry Chardonnay grown in the same climate. From all of the studied countries, wines from Poland seemed to present one of the most characteristic elemental fingerprints since for many elements relatively low levels were recorded. Moreover, this study revealed that also wine samples from USA and Australia can be potentially discriminated from the rest of studied wines. For USA the most characteristic metal for positive identification of the country of origin seems to be uranium, whereases for Australia - strontium and manganese. Based on the highly reduced set of samples, it was not possible to differentiate the studied wine products according to the grape variety other than Syrah, and partially Chardonnay. Since all the Syrah-based samples originated from the same country (Australia) thus, the observed grouping should be more related with the country of origin than the grape variety.

HPLC-MS/MS-based targeted metabolomic method for profiling of malvidin derivatives in dry red wines.

Anthocyanin derivatives are critical components that impart color to aging red wine. In this study, we developed a targeted metabolomic method for the simultaneously profiling of the primary thirty-seven malvidin-derived anthocyanin derivatives in red wine, including various pyranoanthocyanins and flavanols-related condensation products. First, high-performance liquid chromatography (HPLC) tandem ion trap and triple-quadrupole (QqQ) mass spectrometry were used to construct the mass spectral and chromatographic database of the anthocyanin derivatives that were formed in a model wine solution. Next, the targeted profiling analysis of these compounds was achieved on a QqQ mass spectrometer in the multiple reaction monitoring mode (MRM). The method displayed excellent linearity (R2 0.9391-0.9998), sensitivity (0.221-0.604 µg/L of limit of detection (LOD) and 0.274-1.157 µg/L of limit of quantification (LOQ) equivalent to malvidin-3-O-glucoside (Mv-glc)), and repeatability (less than 10% and 15% for intra-day and inter-day relative standard deviation (RSD) respectively). Partial least squares discriminant analysis (PLS-DA) based on this method showed great discrimination over different vintage wines, thereby promising to be an effective tool in wine anthocyanin and aging related study.

Variety features differentiate microbiota in the grape leaves.

The dependence of plant health and crop quality on the epiphytic microbial community has been extensively addressed, but little is known about plant-associated microbial communities under natural conditions. In this study, the bacterial and fungal communities on grape leaves were analyzed by 16S rRNA gene and internal transcribed spacer high-throughput sequencing, respectively. The results showed differences in the composition of the microbial communities on leaf samples of nine wine grape varieties. The most abundant bacterial genus was Pseudomonas, and the top three varieties with Pseudomonas were Zinfandel (22.6%), Syrah (21.6%), and Merlot (13.5%). The most abundant fungal genus was Alternaria, and the cultivar with the lowest abundance of Alternaria was Zinfandel (33.6%), indicating that these communities had different habitat preferences. The linear discriminant analysis effect size of all species showed that the bacteria Enterococcus, Massilia, and Kocuria were significantly enriched on the leaves of Merlot, Syrah, Cabernet Sauvignon, respectively; Pseudomonadales and Pantoea on Zinfandel; and Bacillus, Turicibacter, and Romboutsia on Pinot Noir. Similarly, the fungi Cladosporium, Phoma, and Sporormiella were significantly enriched on Zinfandel, Lon, and Gem, respectively. Both Bray-Curtis and unweighted UniFrac revealed that bacteria and fungi have a significant impact (P < 0.01), and the results further proved that variety is the most important factor affecting the microbial community. The findings indicate that some beneficial or harmful microorganisms existing on the wine grape leaves might affect the health of the grape plants and the wine-making process.

Influence of sequential inoculum of Starmerella bacillaris and Saccharomyces cerevisiae on flavonoid composition of monovarietal Sangiovese wines.

The selection of Starmerella bacillaris strains to be used with Saccharomyces cerevisiae as mixed cultures has been recently suggested in order to produce wines containing lower ethanol and higher glycerol concentrations and to promote fructose degradation due to their fructophilic character. However, studies about effects of such mixed starter cultures on phenolic compounds, which are responsible for the colour and health-enhancing properties in red wines, are currently lacking. Therefore, in this work, the influence of sequential inoculated fermentation (SIF) with Starm. bacillaris and S. cerevisiae on phenolic content of monovarietal Sangiovese wine was evaluated by fermentations at laboratory scale. Axenic fermentations (AXFs) with S. cerevisiae were performed as control. S. cerevisiae attained higher cell densities in AXF compared with SIF. The experimental wines obtained by SIF showed significant lower ethanol and higher glycerol concentrations, whereas no significant difference was detected in colour intensity. The total phenol index reached significantly lower values in SIF. Furthermore, the wines produced by SIF contained higher concentrations of vitisin A that has a greater colour stability than the anthocyanin monomer. Finally, a lower content of both free anthocyanins and flavan-3-ols, key compounds for wine quality possessing also health-enhancing properties, was found in wines obtained by SIF. On the contrary, no significant difference was detected on flavonol concentration between SIF and AXF. This study highlighted that the use of sequential inoculum of Starm. bacillaris and S. cerevisiae can contribute to increasing the colour stability of red wines, even if at the expense of compounds with health properties.

Use of Untargeted Liquid Chromatography-Mass Spectrometry Metabolome To Discriminate Italian Monovarietal Red Wines, Produced in Their Different Terroirs.

The aim of this project was to register, in a liquid chromatography-mass spectrometry-based untargeted single-batch analysis, the metabolome of 11 single-cultivar, single-vintage Italian red wines (Aglianico, Cannonau, Corvina, Montepulciano, Nebbiolo, Nerello, Primitivo, Raboso, Sagrantino, Sangiovese, and Teroldego) from 12 regions across Italy, each one produced in their terroirs under ad hoc legal frameworks to guarantee their quality and origin. The data provided indications regarding the similarity between the cultivars and highlighted a rich list of putative biomarkers of origin wines (pBOWs) characterizing each individual cultivar-terroir combination, where Primitivo, Teroldego, and Nebbiolo had the maximum number of unique pBOWs. The pBOWs included anthocyanins (Teroldego), flavanols (Aglianico, Sangiovese, Nerello, and Nebbiolo), amino acids and N-containing metabolites (Primitivo), hydroxycinnamates (Cannonau), and flavonols (Sangiovese). The raw data generated in this study are publicly available and, therefore, accessible and reusable as a baseline data set for future investigations.

Comparison of Different Categories of Slovak Tokaj Wines in Terms of Profiles of Volatile Organic Compounds.

The present work deals with the characterization of volatile organic compounds (VOCs) in wines from the Slovak Tokaj wine region. Studied wine samples were divided into three groups-varietal wines from registered Tokaj vine varieties, film wines Tokajské samorodné dry, and naturally sweet botrytized wines Tokaj selections. The VOCs from wines were extracted using optimized solid phase microextraction (SPME) and analyzed by comprehensive two-dimensional gas chromatography (GC×GC) coupled to high-resolution time-of-flight mass spectrometry (HRTOF-MS). In total, 176 VOCs were identified in all 46 studied samples. It was found that the total number of VOCs in varietal wines was generally higher than in botrytized wines. All three studied categories showed characteristic VOC profiles with significant differences. Varietal wines were characterized by higher concentrations of esters and terpenoids originating from grapes. The presence of γ-octalactone, (E)-6-methylhept-2-en-4-one, and lack of benzaldehyde were typical for Tokajské samorodné dry. Tokaj selections expressed the highest concentration of diethyl malate, benzaldehyde, and furfurals. Several interesting trends were also observed. The concentration of fermentation products was highest in varietal wines, while long-term matured Tokaj special wines were typified by the presence of compounds related to noble-rotten raisins (2-phenylacetaldehyde, ethyl 2-phenylacetate, and 2-phenylethanol), wood (cis-whisky lactone), and aging (1,1,6-trimethyl-2H-naphthalene, furfural, and 5-methylfurfural).

How much are we exposed to alcohol in electronic media? Development of the Alcoholic Beverage Identification Deep Learning Algorithm (ABIDLA).

BACKGROUND: Evidence demonstrates that seeing alcoholic beverages in electronic media increases alcohol initiation and frequent and excessive drinking, particularly among young people. To efficiently assess this exposure, the aim was to develop the Alcoholic Beverage Identification Deep Learning Algorithm (ABIDLA) to automatically identify beer, wine and champagne/sparkling wine from images. METHODS: Using a specifically developed software, three coders annotated 57,186 images downloaded from Google. Supplemented by 10,000 images from ImageNet, images were split randomly into training data (70 %), validation data (10 %) and testing data (20 %). For retest reliability, a fourth coder re-annotated a random subset of 2004 images. Algorithms were trained using two state-of-the-art convolutional neural networks, Resnet (with different depths) and Densenet-121. RESULTS: With a correct classification (accuracy) of 73.75 % when using six beverage categories (beer glass, beer bottle, beer can, wine, champagne, and other images), 84.09 % with three (beer, wine/champagne, others) and 85.22 % with two (beer/wine/champagne, others), Densenet-121 slightly outperformed all Resnet models. The highest accuracy was obtained for wine (78.91 %) followed by beer can (77.43 %) and beer cup (73.56 %). Interrater reliability was almost perfect between the coders and the expert (Kappa = .903) and substantial between Densenet-121 and the coders (Kappa = .681). CONCLUSIONS: Free from any response or coding burden and with a relatively high accuracy, the ABIDLA offers the possibility to screen all kinds of electronic media for images of alcohol. Providing more comprehensive evidence on exposure to alcoholic beverages is important because exposure instigates alcohol initiation and frequent and excessive drinking.

Resurgence of minority and autochthonous grapevine varieties in South America: a review of their oenological potential.

In contrast with the general trend of producing wine from the most famous grapevine varieties, associated with the French paradigm, such as Cabernet-Sauvignon, Merlot, Pinot Noir, Syrah, Sauvignon Blanc, and Chardonnay, there is a tendency to revalorize and preserve minority or autochthonous grapevine varieties worldwide. The South American wine region, where most of the varieties derived from varieties brought after European colonization, is not exempt from this. This has allowed new wines to be provided with distinctive identities that are markedly different from the current homogeneous wine production. Moreover, varietal homogenization increases vineyard genetic vulnerability in relation to the emergence of grapevine diseases, to which the commonly cultivated varieties are not resistant. This review summarizes the oenological potential of minority or autochthonous grapevine varieties cultivated within the South American wine region, focusing on Argentina, Chile, and Bolivia. © 2019 Society of Chemical Industry.

Study of aromatic profile of different crosses of Monastrell white wines.

BACKGROUND: Aromatic compounds are responsible for the final quality of wines. A semi-quantitative analysis was carried out during two consecutive seasons aiming to determine the volatile composition of 12 new white crosses obtained between Monastrell (M) and other varieties, such as Cabernet Sauvignon (C), Syrah (S) and Tempranillo (T) (MC10, MC180, MC9, MC69, MS30, MS33, MS82, MT103, MT19, MV11, MV67 and MV7), using a methodology based on gas chromatography-solid phase microextraction-mass spectrometry. RESULTS: On the one hand, 30 aromatic compounds were identified belonging to different chemical groups (alcohols, acids, terpenes, norisoprenoids and esters). The results showed how some crosses presented significant differences with respect to their parental. For example, in 2016, Monastrell and Cabernet Sauvignon showed high concentration of alcohols, acids and some terpenes, whereas the corresponding crosses showed a predominance of aromas belonging to esters. In 2017, as a result of edaphoclimatic conditions, the white crosses had higher concentrations of esters and acids. In addition, Monastrell and Cabernet Sauvignon showed similar concentrations of alcohols compared to 2016. On the other hand, sensorial analyses confirmed these results, so that mint and peppermint aromas and a fresh quality were detected in MC69 wine, especially in 2016, and fruity and acid aromas were detected in MC180 wine, which gave it a wide ranging complexity and aromatic potential. CONCLUSION: The present study reports the first investigation of the volatile composition and sensory characteristics of directed crosses white wines obtained from Monastrell and other varieties, such as Cabernet Sauvignon, Syrah, Tempranillo and Verdejo. The results obtained indicate that the use of some of these white crosses could be a good option for growing them in this Mediterranean area as a result of the contribution of a good quality in the wine aroma. © 2019 Society of Chemical Industry.

Chemical typicality of South American red wines classified according to their volatile and phenolic compounds using multivariate analysis.

In this study, 83 wines representating four commercial categories: "Argentinean Malbec", "Brazilian Merlot", "Uruguayan Tannat" and "Chilean Carménère" were analyzed according to their phenolic and volatile compounds. The objective was to identify the chemical compounds that would typify each category. From approximately about 600 peaks obtained by chromatographic techniques, 169 were identified and 53 of them were selected for multivariate statistical analysis. Chilean Carménère was the best discriminated group by the methods applied in our study, followed by Argentinean Malbec. Brazilian Merlot mixed mainly with some Carménère, whileTannat mixed with all wines categories, especially Malbec. In general, Chilean Carménère wines can be characterized by a bluish color, higher amounts of sulphur dioxide, higher content of octanoic acid, isobutanol, ethyl isoamyl succinate and catechin and a smaller amount of quercetin. These data can contribute for further process of authenticity or typification of South American red wines.

Enzymes modulation by dried grape pomace from the manufacture of wines and juices

The processing of grapes for the manufacture of juices and wines, generates large quantities of by-products rich in metabolites with antioxidant, antimicrobial, anti-inflammatory and cicatrizing activities. The high homology between human enzymes and snake venoms makes the latter valuable laboratory tools for the study of pathophysiological processes. Proteases and phospholipases A2 act in processes related to hemostasis and inflammatory response. Thus, in this work, dried pomace obtained from grape (Isabel, Niagara, Bordô, BRS Violeta and Blend cultivars) processing were evaluated on phospholipase, proteolytic, hemolytic and thrombolytic activities induced by snakes venoms and the content of phenolic compounds and minerals was evaluated. The dried pomace exerted inhibitory and potentiating actions in all analyzed activities. The enzymatic modulators present in the evaluated dried pomace have potential for therapeutic use, although their broad characterization is still necessary, in order to define adequate amounts and formulations to obtain efficacy and safety in their use.

Application of fluorescence spectroscopy using classical right angle technique in white wines classification.

The potential of excitation - emission matrices (EEM) measurements using classical right angle technique, in conjunction with chemometrics, was prospected for white wine classification with respect to their cultivar and geographical origin. For this purpose, wines belonging to four cultivars (Chardonnay, Pinot Gris, Riesling and Sauvignon) from two different countries (Romania and France) were investigated. The excitation - emission matrices were statistically processed using parallel factor analysis (PARAFAC). According to Soft Independent Modeling Classification Analogy (SIMCA) model, for cultivar differentiation, only 3 out of 107 wine samples (1 Pinot Gris (Romania); 1 Riesling (Romania) and 1 Sauvignon (France)) were misclassified while for geographical origin assessment, only 2 wines (1 Romania and 1 France) were misclassified. This study  demonstrates the potential of excitation - emission fluorescence matrices spectroscopy using the classical right angle technique in wine authentication, without sample dilution.

Volatile terpenoids, norisoprenoids and benzenoids as markers of fine scale vineyard segmentation for Corvina grapes and wines.

In this study, the diversity existing at the very small scale of single vineyard parcels in volatile composition of grapes and wines from a single estate in the Valpolicella wine region has been studied. Corvina grapes from eight contiguous vineyards were used for the study and vinified with the same protocol. The compounds analyzed by GC-MS were representative of the terpenoid, norisoprenoid and benzenoid chemical families. Free and bound compounds analysis showed that differences between parcels were relatively small on grapes samples, whereas after fermentation larger differences between wine samples were highlighted. Multivariate statistical analysis of wine volatiles highlighted the existence of similarities between wine volatile profiles, which reflected to a good extent the geographical location of the corresponding vineyard parcels. The main drivers of this diversity were the monoterpene alcohols linalool, α-terpineol, linalool oxide; the benzenoids vanillin, ethyl vanillate and methyl vanillate; and the norisoprenoid ß-damascenone. Wine from one vineyard parcel was not correctly classified, possibly due to the influence of the peculiar training system applied to this parcel. With aging the vineyard parcel geographical diversity was still reflected by the chemical diversity of wines, even if the separation was less fine. As many reactions occurred, some drivers of the diversity were changed after aging. They were benzenoids: ethyl vanillate, methyl vanillate and vanillin; the norisoprenoid 3-oxo-α-ionol; the terpenes linalool oxide, linalool, p-methane-1,8-diol, α-terpineol, and the precursors of nerol, geraniol, linalool.