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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., Competing Interests: Declaration of Competing Interest There is no conflict of interest to declare., (Copyright © 2020 Elsevier B.V. All rights reserved.)

In the present study, label-free SERS spectroscopy is applied as a useful analytical technique for white wine characterization. 180 samples of three white wines varieties from northeastern Italy, Sauvignon Blanc, Ribolla Gialla and Friulano, collected from three different Italian producers from 2016 vintage, have been analyzed using Ag citrate-reduced colloids and a portable Raman instrument with a 785 nm laser. A PCA of SERS spectra showed that discrimination between wines and wineries is possible. Main spectral differences are due to adenine, carboxylic acids and glutathione, with their ratio changing among different wine types and producers. A robust version of the Soft Independent Modelling of Class Analogy (SIMCA) method was used to model the class space of each wine and to perform the classification among the different categories, yielding overall efficiencies between 87 and 93%. These results are extremely encouraging and open the way to the application of this SERS protocol as a wine identification assay., (Copyright © 2019 Elsevier B.V. All rights reserved.)

The feasibility of rapid detection of three quality parameters and classification of wines based on visible and near infrared spectroscopy (Vis-NIRs) was investigated. A modified ant colony optimization (ACO) algorithm for wavelength selection in Vis-NIR spectral analysis was proposed to improve the prediction performance of partial least squares regression (PLSR) model. The result proved that feature wavelengths/variables can be selected by the proposed method for building a high performance PLSR model. The root mean square error of total acid, total sugar and alcohol obtained by ACO-PLS were 0.00122 mol/l, 0.0893 g/l and 0.206 (v/v), respectively. Their correlation coefficients obtained by ACO-PLS reach to 0.973, 0.994 and 0.928, respectively. Compared with full-spectral PLS and PLS based on competitive adaptive reweighted sampling (CARS-PLS) method, the application of ACO wavelength selection provided a notably improved regression model. The prediction results were significantly better than the full-spectral PLS model and slightly better than the CARS-PLS method. Meanwhile, a classification study was also constructed based on the ACO-Principal component analysis (ACO-PCA) model showed that Vis-NIR spectra could be used to classify wines according to the geographical origins. Therefore, it can be concluded that the Vis-NIR spectroscopy technique based on ACO wavelength selection has high potential to differentiate the wine origins and predict the quality parameters in a nondestructive way., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively., (Copyright © 2018 Elsevier B.V. All rights reserved.)

This work reports on a simple and fast classification procedure for the quality control of red wines with protected designation of origin (PDO) by means of Laser Induced Breakdown Spectroscopy (LIBS) technique combined with Neural Networks (NN) in order to increase the quality assurance and authenticity issues. A total of thirty-eight red wine samples from different PDO were analyzed to detect fake wines and to avoid unfair competition in the market. LIBS is well known for not requiring sample preparation, however, in order to increase its analytical performance a new sample preparation treatment by previous liquid-to-solid transformation of the wine using a dry collagen gel has been developed. The use of collagen pellets allowed achieving successful classification results, avoiding the limitations and difficulties of working with aqueous samples. The performance of the NN model was assessed by three validation procedures taking into account their sensitivity (internal validation), generalization ability and robustness (independent external validation). The results of the use of a spectroscopic technique coupled with a chemometric analysis (LIBS-NN) are discussed in terms of its potential use in the food industry, providing a methodology able to perform the quality control of alcoholic beverages., (Copyright © 2016 Elsevier B.V. All rights reserved.)

To evaluate the taste characteristics of Chinese rice wine, wine samples sourced from different vintage years were analyzed using liquid chromatographic analysis, sensory evaluation, and an electronic tongue. Six organic acids and seventeen amino acids were measured using high performance liquid chromatography (HPLC). Five monosaccharides were measured using anion-exchange chromatography. The global taste attributes were analyzed using an electronic tongue (E-tongue). The correlations between the 28 taste-active compounds and the sensory attributes, and the correlations between the E-tongue response and the sensory attributes were established via partial least square discriminant analysis (PLSDA). E-tongue response data combined with linear discriminant analysis (LDA) were used to discriminate the Chinese rice wine samples sourced from different vintage years. Sensory evaluation indicated significant differences in the Chinese rice wine samples sourced from 2003, 2005, 2008, and 2010 vintage years in the sensory attributes of harmony and mellow. The PLSDA model for the taste-active compounds and the sensory attributes showed that proline, fucose, arabinose, lactic acid, glutamic acid, arginine, isoleucine, valine, threonine, and lysine had an influence on the taste characteristic of Chinese rice wine. The Chinese rice wine samples were all correctly classified using the E-tongue and LDA. The electronic tongue was an effective tool for rapid discrimination of Chinese rice wine., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Three independent methodologies were investigated to achieve the differentiation of red grapes from different grape varieties (Garnacha, Graciano, Mazuelo and Tempranillo) collected from five vineyards located in the D.O.Ca. Rioja. Anthocyanin chromatographic analysis, color image analysis and near infrared hyperspectral imaging were carried out for the grapes. Then, a Stepwise Linear Discriminant Analysis (SLDA) was developed for each data set in order to discriminate grapes according to their grape variety. As a result, using anthocyanin profile, color image analysis and near infrared hyperspectral imaging respectively, 88%, 54% and 100% of the samples were correctly classified in the internal validation process and 86%, 52% and 86% were correctly classified in the leave-one-out cross-validation process., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Biogenic amines are nitrogenous organic compounds of low molecular weight that are either formed or metabolized in cells of living organisms and can be found in several food products, being produced mainly by amino acid decarboxylation. When ingested in high concentrations they can induce several health problems in humans. In alcoholic beverages, and especially in wine, they are formed during the vinification process as a result of the action of microorganisms. In this work it is proposed a new methodology for the determination of biogenic amines in wines, which includes a sample preparation approach based on salting-out assisted liquid-liquid extraction, the use of dansyl chloride for the derivatization and chromatographic separation by high-performance liquid chromatography with fluorimetric detection. The salting-out effect is used to promote phase separation between water and a water-miscible organic solvent, while improving the extraction of organic or inorganic species. Several extraction parameters were optimized, such as the dansyl chloride concentration, pH and the effects caused by the order in which the extraction and derivatization were performed. Extraction of amines, and consequent detection, depends on the presence of dansyl chloride in solution prior to extraction. The results showed the possibility to simultaneously perform the extraction and the derivatization, making sample preparation easier and less time-consuming. The methodology was successfully applied to the determination of biogenic amines in five wines (white, red and rosé). This method has the potential to be a good alternative to existing methods since it is cheaper, easier and simplifies the sample preparation step., (Copyright © 2014 Elsevier B.V. All rights reserved.)

A dynamic headspace sorptive extraction (DHS) combined with thermal desorption (TD) and coupled with gas chromatography-mass spectrometry (GC/MS) was developed for the determination of 11 esters which contribute to the fruity aroma in sweet wines. A full factorial (4 factors, 2 level) experiment design was used to optimize the extraction conditions and the results were evaluated by multiple linear regression (MLR) and principal component analysis (PCA). The esters showed optimal extraction using an extraction temperature of 30°C during 20 min, and a subsequent purge volume of 300 mL and dry volume of 50 mL. Afterwards, quantification was achieved using calibration curves constructed for each ester with linear regression equations having correlation coefficients (R(2)) ranging from 0.9894 to 0.9981. The proposed method was successfully validated and showed good intermediate precision, repeatability and accuracy values for all the monitored compounds. Finally, the method was applied to quantify esters, with fruity aromatic notes, of sweet white and red wines, elaborated with different winemaking processes., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Tartaric instability of wines still represents a serious problem in terms of the commercial value of bottled wines, particularly whites, leading consumers to be suspicious as regards the effective healthiness or wholesomeness of products. The study, carried out on 536 Italian wines, investigated the potential of using Fourier Transform Infrared Spectroscopy, distinguishing between white and red/rosé wines, to create models predicting the instability of wines, assessed in comparison to two of the most widespread methods of reference: the "mini-contact test" (10 min, 0 °C, KHT) and the "cooling test" (5 days, -4 °C). The models proposed, constructed using 80% of the samples and based on Partial Least Squares-Regression and Artificial Neural Networks, were shown to work well in terms of correct classification (from 89% to 97%) of the external validation subset (20%). As regards the more problematical question of technical management of wines before bottling, in the worst cases only 4-6% of unstable samples were erroneously classified as stable., (© 2013 Elsevier B.V. All rights reserved.)

This work reports the application of a BioElectronic Tongue (BioET) in the estimation of polyphenol content in wine. The approach used an array of enzyme biosensors capable of giving a wide and complete response of the analyzed species, plus a chemometric processing tool able to interpret the chemical signals and extract meaningful data from the complex readings. In our case, the proposed BioET was formed by an array of four voltammetric enzymatic biosensors based on epoxy-graphite composites, one blank electrode and the other three bulk-modified with tyrosinase and laccase on one side, and copper nanoparticles on the other; these modifiers were used in order to incorporate differentiated or catalytic response to different polyphenols present in wine and aimed to the determination of its total polyphenol content value. The obtained voltammetric responses were pre-processed employing the Fast Fourier Transform (FFT); this was used to compress the relevant information whereas the obtained coefficients fed an Artificial Neural Network (ANN) model that accomplished the quantification of total polyphenol content. For comparison purposes, obtained polyphenol content was compared against the one assessed by two different reference methods: Folin-Ciocalteu and UV polyphenol index (I(280)); good prediction ability was attained with correlation coefficients higher than 0.949 when comparing against reference methods. Qualitative discrimination of individual polyphenols found in wine was also assessed by means of Principal Component Analysis which allowed the discrimination of the individual polyphenols under study., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Five cherry wines exhibiting marked differences in taste and mouthfeel were selected for the study. The taste and mouthfeel of cherry wines were described by four sensory terms as sour, sweet, bitter and astringent. Eight organic acids, seventeen amino acids, three sugars and tannic acid were determined by high performance liquid chromatography (HPLC). Five phenolic acids were determined by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS). The relationship between these taste-active compounds, wine samples and sensory attributes was modeled by partial least squares regression (PLSR). The regression analysis indicated tartaric acid, methionine, proline, sucrose, glucose, fructose, asparagines, serine, glycine, threonine, phenylalanine, leucine, gallic acid, chlorogenic acid, vanillic acid, arginine and tannic acid made a great contribution to the characteristic taste or mouthfeel of cherry wines., (Copyright © 2012 Elsevier B.V. All rights reserved.)

The variables affecting the direct matrix assisted laser desorption ionization mass spectrometry-based analysis of wine for classification purposes have been studied. The type of matrix, the number of bottles of wine, the number of technical replicates and the number of spots used for the sample analysis have been carefully assessed to obtain the best classification possible. Ten different algorithms have been assessed as classification tools using the experimental data collected after the analysis of fourteen types of wine. The best matrix was found to be α-Cyano with a sample to matrix ratio of 1:0.75. To correctly classify the wines, profiling a minimum of five bottles per type of wine is suggested, with a minimum of three MALDI spot replicates for each bottle. The best algorithm to classify the wines was found to be Bayes Net., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Hyphenation of micro-liquid and ultra-performance liquid chromatography with a hybrid (QqTOF) tandem mass spectrometry was studied from the viewpoint of their applicability for monitoring of anthocyanin dyes in various cultivars and different vintages of red wine. After appropriate optimization both techniques proved to be suitable for this task. UHPLC system provided lower LOD and LOQ values as well as higher productivity and precision of retention parameters and peak areas with respect to micro-LC method. On the other hand, micro-LC method offers significant savings of mobile phase, less contamination of ion source of mass spectrometer and makes the nebulization process in electrospray much easier. In combination with principal component analysis (PCA), the method allows to elucidate long-term relations in a complete set of certified red wine cultivars grown in South Moravia (certified in Czech Republic until year 2005). Among the studied varieties Rubinet exhibit a specific anthocyanin profile. Especially interesting is a high content of 3-coumaroylglucoside-5-glucosides of malvidin and peonidin. Those dyes can be used as markers of artificial color enhancement. Principal component analysis applied to data processed with regard to enzymatic activity markedly facilitate classification among varieties. This approach allows resolution of teinturiers, variety Blaufränkish and partial classification of other varieties., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Head-space solid-phase microextraction (HS-SPME) coupled to gas-chromatography-mass spectrometry was developed and applied to obtain the volatile aromatic fingerprints of three typical Italian wines, Valpolicella, Amarone and Recioto, all produced in the restricted geographical area of Valpolicella (Veneto, Italy) with the same grape cultivars within the regulations of a rigid disciplinary of production. Differences between the three typologies are mainly linked to the different withering times to which grapes are subjected before vinification, which strongly influences the concentration and the development of volatile aroma compounds. A total of 22 different wines (7 Valpolicella, 10 Amarone and 5 Recioto) were characterised in terms of aromatic volatile profile with the aim to distinguish the different products and to evaluate the possibility to differentiate the same product from different brands. For the chemometric evaluation of the data one-way analysis of variance (ANOVA), principal component analysis (PCA) and hierarchical cluster analysis (HCA) were tested. All the chemometric tools employed allow to differentiate between the three products. More intriguing is the ability of the chemometric approach to differentiate between the same product (Amarone, Recioto) from different winery, thus showing the potential of this approach to characterize the brand-dependent typicality of wines, which is usually related to subtle technological differences which nevertheless have strong influences on the organoleptic characteristics of the products., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Solid-phase extraction cartridges (SPE)-GC/MS method was used to analyse red wines aromas. The matrix effect was studied with chemicals standard prepared in synthetic wines with water/alcohol solutions (12% ethanol, v/v) following the procedure proposed. The method offers good reproducibility since the relative standard deviations (RSD%) for the volatile compounds levels were less than 9%. This method was used to differentiate the aroma of one hundred mono-varietal young, crianza, reserva and gran reserva La Mancha D.O. wines (cv. Tempranillo) on the basis of oak barrel contact period. Samples were checked at ten time points over 36 months. Sixty important wine odorants, such as volatile phenols, vanillin derivatives, lactones, norisoprenoids, benzene compounds, esters and terpenols, can be quantitatively determined in a single run. Results showed significant quantitative differences in the volatile profiles of wines depending on the length of time in contact with wood, even in wines belonging to the same commercial category. Stepwise multiple linear regression (SMLR) was used to obtain a model that predicted the time of barrel ageing to which wines were submitted in relation with the wine volatile composition. A successful function based on eight compounds with a mean deviation of 0.37 months in the predictions, was obtained., (Published by Elsevier B.V.)

The potential of a vanguard technique as is the ion mobility spectrometry with ultraviolet ionization (UV-IMS) coupled to a continuous flow system (CFS) have been demonstrated in this work using a gas phase separator (GPS). This vanguard system (CFS-GPS-UV-IMS) has been used for the analysis of different types of white wines to obtain a characteristic profile for each type of wine and their posterior classification using different chemometric tools. Precision of the method was 3.1% expressed as relative standard deviation. A deep chemometric study was carried out for the classification of the four types of wines selected. The best classification performance was obtained by first reducing the data dimensionality by principal component analysis (PCA) followed by linear discriminant analysis (LDA) and finally using a k-nearest neighbour (kNN) classifier. The classification rate in an independent validation set was 92.0% classification rate value with confidence interval [89.0%, 95.0%] at 95% confidence level. The same white wines analyzed using CFS-GPS-UV-IMS were analyzed using gas chromatography with a flame detector (GC-FID) as conventional technique. The chromatographic method used for the determination of superior alcohols in wine samples shown in the Regulation CEE 1238/1992 was selected to carry out the analysis of the same samples set and later the classification using appropriate chemometrics tools. In this case, strategies PCA-LDA and kNN classifier were also used for the correct classification of the wine samples. This combination showed similar results to the ones obtained with the proposed method., (Copyright © 2011 Elsevier B.V. All rights reserved.)

In this study, the potential of high performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (HPLC-QTOFMS) for metabolomic profiling of red wine samples was examined. Fifty one wines representing three varieties (Cabernet Sauvignon, Merlot, and Pinot Noir) of various geographical origins were sourced from the European and US retail market. To find compounds detected in analyzed samples, an automated compound (feature) extraction algorithm was employed for processing background subtracted single MS data. Stepwise reduction of the data dimensionality was followed by principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) which were employed to explore the structure of the data and construct classification models. The validated PLS-DA model based on data recorded in positive ionization mode enabled correct classification of 96% of samples. Determination of molecular formula and tentative identification of marker compound was carried out using accurate mass measurement of full single MS spectra. Additional information was obtained by correlating the fragments obtained by MS/MS accurate mass spectra using the QTOF with collision induced dissociation (CID) of precursor ions., (Copyright © 2010 Elsevier B.V. All rights reserved.)

In the present study, direct flow injection mass spectrometry was investigated for rapid characterization of the polyphenolic composition of red wines. Atmospheric pressure chemical ionization (APCI) and electrospray ionization (ESI) (in both positive and negative ion modes) have been simultaneously used for a more comprehensive analysis of the samples studied. In this way, four mass spectra have been recorded for each wine. Each spectrum was considered as a fingerprint related to the chemical composition. This methodology was applied to a large number of Beaujolais wines from different grades and different vintages. This data set was processed using a chemometrical multiblock analysis, which allowed to synthesize the whole information collected. The results obtained showed that the wine fingerprints address the composition of the main polyphenolic compounds present in the red wines and can discriminate groups of wines showing different polyphenolic compositions. Multiblock analysis appears as a very promising tool to deal with several data tables of multivariate signals in order to define, by combining the whole information, the best operating protocol according to the desired analytical objectives., (Copyright © 2010 Elsevier B.V. All rights reserved.)

This work was undertaken to evaluate whether it is possible to determine the variety of a Chinese wine on the basis of its volatile compounds, and to investigate if discrimination models could be developed with the experimental wines that could be used for the commercial ones. A headspace solid-phase microextraction gas chromatographic (HS-SPME-GC) procedure was used to determine the volatile compounds and a blind analysis based on Ac/Ais (peak area of volatile compound/peak area of internal standard) was carried out for statistical purposes. One way analysis of variance (ANOVA), principal component analysis (PCA) and stepwise linear discriminant analysis (SLDA) were used to process data and to develop discriminant models. Only 11 peaks enabled to differentiate and classify the experimental wines. SLDA allowed 100% recognition ability for three grape varieties, 100% prediction ability for Cabernet Sauvignon and Cabernet Gernischt wines, but only 92.31% for Merlot wines. A more valid and robust way was to use the PCA scores to do the discriminant analysis. When we performed SLDA this way, 100% recognition ability and 100% prediction ability were obtained. At last, 11 peaks which selected by SLDA from raw analysis set had been identified. When we demonstrated the models using commercial wines, the models showed 100% recognition ability for the wines collected directly from winery and without ageing, but only 65% for the others. Therefore, the varietal factor was currently discredited as a differentiating parameter for commercial wines in China. Nevertheless, this method could be applied as a screening tool and as a complement to other methods for grape base liquors which do not need ageing and blending procedures., (2010 Elsevier B.V. All rights reserved.)

HPLC with UV and acidified potassium permanganate chemiluminescence detection, combined with multivariate data analysis techniques, were used for the geographical classification of some Australian red (Cabernet Sauvignon) and white (Chardonnay) wines from two regions (Coonawarra and Geelong). Identification of the wine constituents prominent in the chromatography was performed by mass spectrometry. Principal components analysis and linear discriminant analysis were used to classify the wines according to region of production. Separation between regions was achieved with both detection systems and key components leading to discrimination of the wines were identified. Using two principal components, linear discriminant analysis with UV detection correctly classified 100% of the Chardonnay wines and, overall 91% of the Cabernet Sauvignon wines. With acidified potassium permanganate chemiluminescence detection, 75% of the Chardonnay wines and 94% of the Cabernet Sauvignon wines were correctly classified using two factors.

Analysis of 34 Sauvignon Blanc wine samples from three different countries and six regions was performed by gas chromatography-mass spectrometry (GC-MS). Linear discriminant analysis (LDA) showed that there were three distinct clusters or classes of wines with different aroma profiles. Wines from the Loire region in France and Australian wines from Tasmania and Western Australia were found to have similar aroma patterns. New Zealand wines from the Marlborough region as well as the Australian ones from Victoria were grouped together based on the volatile composition. Wines from South Australia region formed one discrete class. Seven analytes, most of them esters, were found to be the relevant chemical compounds that characterized the classes. The grouping information obtained by GC-MS, was used to train metal oxide based electronic (MOS-Enose) and mass spectrometry based electronic (MS-Enose) noses. The combined use of solid phase microextraction (SPME) and ethanol removal prior to MOS-Enose analysis, allowed an average error of prediction of the regional origins of Sauvignon Blanc wines of 6.5% compared to 24% when static headspace (SHS) was employed. For MS-Enose, the misclassification rate was higher probably due to the requirement to delimit the m/z range considered.

Nine different types of Italian red wines of four different varieties were analysed, without any sample pre-treatments, by voltammetric techniques using a poly(3,4-ethylenedioxythiophene)-modified electrode. The data matrices consisting of the currents measured at different potentials, by repeated Cyclic Voltammetry or Differential Pulse Voltammetry, are submitted to chemometric analysis. After explorative tests based on Principal Component Analysis, Partial Least Squares-Discriminant Analysis classification models are built both for the training and for the test sets. To this aim, different classification strategies are adopted, considering the responses from the two techniques either separately or joined together to form a data matrix including the whole voltammetric information.

Solid-supported liquid-liquid extraction (SS-LLE) was compared to liquid-liquid extraction (LLE) for the analysis of phenolic compounds in wine. Diatomaceous earth commercial cartridges were evaluated together with "in-house" made cartridges for the wine phenolic extraction. Statistical treatment, analysis of variance ANOVA-single factor, was used to compare the extraction yields obtained by these methods, and for the majority of the studied compounds, significantly higher yields were obtained by the SS-LLE methodology using the "in-house" prepared cartridges. This is an environmentally friendly low-cost sample preparation method which proved to be reproducible (RSD<5% for the most compounds) and yielding high recoveries (80-100%) for the compounds studied.

The polyphenols (some of them are also called phytoalexins, flavonols, flavanons, flavanonols, flavons, flavanols, and anthocyanines) are usually marked as potent antioxidants or radical scavengers which assist the body cells against oxidation. Polyphenols in wine are also considered to explain so called French paradox (long life aging and low number of coronary diseases despite of high alcohol and fat consumption). The total polyphenolic content (TPC) and total antioxidant potential (TAP) were determined by photometry and found strongly correlated. This finding suggests that the determination of TAP can be replaced by a more simple procedure of TPC determination. Capillary zone electrophoresis (CZE) with preconcentration by solid phase extraction (SPE) was applied for some polyphenols determination and for obtaining electropherograms of the SPE extracts (fingerprints). From mathematical evaluation of the fingerprints, prediction of cultivars and vintage using artificial neural networks (ANN) was done with more than 90% correct prediction. The study was performed on a set of 47 samples of young wines (vintage 1999-2002) from south Moravia (Czech Republic) and New South Wales (Australia).

The system based on coupling a headspace sampler to a mass spectrometer (HS-MS), considered a kind of electronic nose (e-nose), is an emerging technique in the field of food aroma analysis. The global mass spectrum this system provides is a fingerprint of each sample analysed that contains the information related to volatile composition of the sample. The use of chemometric techniques allows to compare the spectra of the samples and then, to classify them according to different properties. In this paper, we present the development of a method for wine analysis using a HS-MS system and multivariate analysis techniques. The method was successfully applied to differentiate and classify wines according to its origin, variety and ageing. The main advantages of the proposed methodology are the minimum sample preparation required and the speed of analysis (10 min/sample).

A rapid HPLC-diode array detection (DAD) method was developed for the routine analysis of 16 anthocyanins in wine. Direct injection of filtered wine samples followed by selective detection at 520 nm allowed quantitation of these compounds in red wines. The method was linear for malvidin-3-glucoside over the range 5-250ppm, and the limit of detection for this compound was 0.18 ppm. A volatile mobile phase is used, which enables hyphenation to mass spectrometry (MS). With HPLC-MS, a total of 44 pigments could be identified in South African wines. Obtained mass spectra are discussed for a series of representative wine constituents and results are compared with literature references. An attempt was made to differentiate between different cultivars according to the anthocyanin content using stepwise forward linear discriminant analysis (LDA).

Sixty-eight samples of wines from Piedmont (Italy) were analysed to determine their content of volatile compounds, using the solid-phase microextraction (SPME) technique coupled with gas chromatography-mass spectrometry (GC-MS). Samples were from five groups of wines: Barolo, Barbaresco, Nebbiolo d'Alba, Roero and Langhe Nebbiolo, all produced from the Nebbiolo grape in the Langhe and Roero areas (province of Cuneo, Piedmont) but differing in vintage (respectively, 3 years, 2 years, 1 year, 8 months and few months) and production zone. Thirty-five analytes were identified; peak area data, corrected for internal standard, were used for pattern recognition treatments. Principal components analysis, hierarchical cluster analysis, Kohonen self organising map, stepwise linear discriminant analysis and soft independent modelling of class analogy were applied to the data, revealing a good separation between the five groups. A main factor, strictly connected to wine vintage, was identified and found to be related to some analytes.

A HPLC method that allows the separation of several anthocyanins present in red grapes and red wines, using a linear gradient of acetonitrile in water at pH 1.3, using perchloric acid as an acid modifier, is described. Data clearly show that the anthocyanins profile of red grapes may be complex, but quite different for each cultivar studied. Thus, those molecules may be used as chemotaxonomic markers for classifying red grape cultivars. However, the anthocyanin profile of red wines clearly differs from that presented by grapes employed in making it, because red wine contains a higher relative amount of malvidin-3-O-glucoside than grapes, and the relative amount of other anthocyanins in wines is usually lower than in grapes. Therefore, the use of anthocyanins present in wines to determine the grape cultivar used for winemaking needs a careful evaluation of the influence of different technological procedures on the anthocyanins fingerprint.

The adsorption and desorption capacities of 11 different solid-phase extraction sorbents were tested for the preconcenration of pigments of various Hungarian red wines. The concentrates were evaluated by multiwavelengh spectrophotometry combined with a spectral mapping technique (SPM) and by reversed-phase high-performance liquid chromatography. The highest (10-fold) concentration of pigments was achieved on octadecylsilica sorbent. It can be used five times without losing adsorption and desorption characteristics. SPM indicated that multiwavelength spectrophotometry can be employed for the differentiation of red wines. Comparison of the chromatograms of pigments with and without preconcentration showed that preconcentration makes possible the separation and detection of pigments present in low concentration in red wines.

A simple and fast HPLC method without sample pretreatment is described for the separation of anthocyanins in red wines using a new pH-stable stationary phase. The linearity between peak area and concentration and ruggedness of the method were checked. Investigations were made about the safekeeping of red wine samples concerning anthocyanins. Classification of 52 different wine samples was performed by multivariate statistical methods.

Solid-phase extraction with a novel porous carbon sorbent CARB GR was used for the clean-up step of dicarboxyimide fungicides residues from variety of Slovak grape wines with subsequent capillary gas chromatography-flame ionization detection, -electron-capture detection (ECD) and -mass spectrometry-ion-trap detection (MS-ITD) analysis. Recovery was tested at various concentration levels of vinclozolin and iprodione in standard solutions (R = 80-97%, R.S.D. < or = 5). The value of recovery in spiked wines is dependent on concentration level (studied in the range of 5.9 micrograms/1-1.96 mg/l) and on the variety of wine (R = 80-96%; R.S.D. = 3-5%). Limits of quantitation (for sample volume 50 ml) were determined to be with GC-ECD for both fungicides in ppt range and with GC-MS-ITD in the multiple ion detection mode monitoring in ptt range for vinclozolin and ppb range for iprodione. Concentration levels of vinclozolin residues were determined in treated wines (with 0.1% Ronilan 50 WP) as well as iprodione residues (with 0.15% Rovral 50 WP) and a strong dependence on the protective term before the harvest is shown.