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In order to facilitate the high-quality advancement and digital innovation of the wine industry, a method based on principal component analysis (PCA) and improved quantum particle swarm optimization (IQPSO) to optimize support vector machine (PCA-IQPSO-SVM), was proposed to solve the wine classification problem. First, the feature extraction ability of PCA was used to reduce the input dimension of the model and improve the classification efficiency. At the same time, aiming at the problems that the quantum particle swarm optimization (QPSO) is easy to fall into local optimum and the convergence ability is decreased in the later stage of optimizing SVM, a variety of improvement strategies are used to improve QPSO to find the best parameters of SVM. The experimental results demonstrate that the model of PCA-IQPSO-SVM exhibits superior evaluation indices compared to other models. Moreover, the optimization efficiency of the PCA-IQPSO-SVM model is enhanced by 1.64% to reach an impressive 85.2%, showcasing its remarkable optimization effect. Simultaneously, this study provides a scientific approach for quality classification in the wine industry, thereby facilitating its high-quality development. [ABSTRACT FROM AUTHOR]

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 ( 1 H 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 1 H NMR spectra combined with a data fusion strategy are practical for the classification of different vintages of Maotai-flavor Baijiu.

Attenuated total reflectance Fourier-transform infrared spectrometry (ATR FTIR) was applied for the determination of the content in wines without the use of complicated sample preparation. This simple and rapid approach was suitable for the characterization of twelve Slovak varietal wines with a protected designation of origin (PDO). Middle infrared region 4000—600 cm−1 was used for further processing by methods of multidimensional data analysis, namely principal component analysis (PCA) and linear discriminant analysis (LDA). PCA was applied for the effective reduction of the original number of input wavenumbers as well as for the identification of important variables. The goal of the discriminant analysis was to achieve maximal discrimination among the wine categories. Its task was to create a discriminant model from the original variables capable to categorize the unlabeled wines into predefined groups with the classifier. After it, the ATR FTIR analysis together with the chemometric approach proved to be a powerful tool for discrimination of wine categories and for evaluation of the authenticity. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new chemometric approach for wines classification is developed. • Fractal dimensions adequately discriminated all wine varietals from each other. • The method descripted meets most of the Green Analytical Chemistry requirements. A study of Argentine wines was carried out by direct injection of the samples without previous treatment in a high-performance liquid chromatography system coupled to a diode array detector (HPLC-DAD). The collected data, corresponding to six different varietals (Bonarda, Cabernet Sauvignon, Malbec, Merlot, Syrah, and Tempranillo), were processed with a new chemometric approach: determining the fractal dimension of principal components or fractal dimension analysis (FDA). A comparative study of the efficiency of the new proposed model to discriminate wine samples according to botanical origin was made with methods based on a linear multivariate resolution curve-alternating least squares model (MCR-ALS) previously reported. The results show that the fractal dimension analysis of the data improves the discrimination between all the varietals studied, allowing better discrimination in relation to other reported methodologies. Finally, a comparative analysis of the method's sustainability was carried out using the Analytical Eco-Scale indicator. This analysis demonstrated that the method based on fractal analysis is a simple procedure that meets most of the Green Analytical Chemistry requirements. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to classify wines from 4 different countries in South America. Each class of wines is formed by samples considered by experts as representatives of the following commercial categories: “Argentinean Malbec (AM)”, “Brazilian Merlot (BM)”, “Uruguayan Tannat (UT)” and “Chilean Carménère (CC)”. The 83 samples collected were analyzed according to their composition of volatiles, semi-volatiles and phenolic compounds. We built a decision system for classification based on support vector machines (SVM), along with Correlation-based Feature selection (CFS), and Random Forest Importance (RFI), which measures the relative importance of the input variables. First, we use CFS to select a subset of variables among 190 chemical compounds. Thirteen chemicals were selected as correlated to the category and uncorrelated with each other. Afterwards, these chemical compounds were organized according to the importance ranking given by the RFI and classified with SVM. The study clearly indicated that SVM in combination with feature selection methods was able to identify the most important chemicals to classify the wine samples. Among the compounds identified in the wine samples, the variable subset defined by the feature selection methods, which were catechin, gallic, octanoic acid, myricetin, caffeic, isobutanol, resveratrol, kaempferol, and ORAC, were able to achieve an accuracy of 93.97% in classifying the commercial categories.

The wine sector in Italy is a significant contributor to the country's economy. Furthermore, Italy is renowned for producing some of the world's most excellent wines. For these reasons, the wine industry is susceptible to frauds, such as reporting false origin claims in labels. In this context, this paper aims to provide traceability and quality tool to recognise wines from different Italian regions, i.e. Calabria, Piemonte, Sicily and Veneto, from selected winemakers that reach the Top 10% in almost three international rankings. The multi-elemental analysis of soil and wine samples by ICP-MS and their chemometric treatments have identified Ni, Cs, Li, Ca, Ba, Pb, Rb, Sb, Fe and U as the most informative variables to reach optimal classification of samples. The analytical data have been used to develop an original managerial framework to leverage Pareto's Principle for effective brand-land management in the top wine segment. • Soil and wine samples from selected top vinery have been analysed by ICP-MS. • Chemometric treatment (PCA, variables selection, classification) were employed. • Ten variables were identified to recognise samples from geographical origin. • Pareto's Principle was applied on data for top wine management use. [ABSTRACT FROM AUTHOR]

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.

Wines with geographical indication can be classified and represented by such features as designations of origin, producers, vintage years, alcoholic strength, and grape varieties; these features allow us to define wines in terms of a set of necessary and/or sufficient conditions. However, wines can also be identified by other characteristics, involving their look, smell, and taste; in this case, it is hard to define wines in terms of necessary and/or sufficient conditions, as wine concepts exhibit typicality effects. This is a setback for the design of computer science ontologies aiming to represent wine concepts, since knowledge representation formalisms commonly adopted in this field do not allow for the representation of concepts in terms of typical traits. To solve this problem, we propose to adopt a hybrid approach in which ontology-oriented formalisms are combined with a geometric representation of knowledge based on conceptual spaces. As in conceptual spaces, concepts are identified in terms of a number of quality dimensions. In order to determine those relevant for wine representation, we use the terminology developed by the Italian Association of Sommeliers to describe wines. This will allow us to understand typicality effects about wines, determine prototypes and better exemplars, and measure the degree of similarity between different wines. [ABSTRACT FROM AUTHOR]

In this paper, samples of Cabernet Sauvignon wines produced in California have been analyzed on the basis of their elemental content and classified according to its geographical origin by the use of machine learning. Overall, 13 metals (Al, Cd, Co, Cr, Cu, Li, Mn, Ni, P, Pb, Rb, Sr, and Zn) were determined by inductively coupled plasma mass spectrometry (ICP-MS). We used two algorithms of variable selection in order to estimate the relevance of each metal to classification. Predictive models based on chemometric tools and machine learning algorithms were developed to differentiate origin of wine samples. Li and Sr were identified as the main responsible for the differentiation of samples. The application of Random Forest permitted to correctly classify all samples. A second analysis was performed by removing the variables Li and Sr to investigate the relevance of the others metals. We found that a group of seven variables (Cd, Ni, Mn, Pb, Rb, Co, Cu) which were able to discriminate the wines in 89% of accuracy by using Support Vector Machines. Results suggested that the developed methodology by advanced machine learning techniques is robust and reliable for the geographical classification of wine samples, and the study of the elements that characterize the regions. [ABSTRACT FROM AUTHOR]

Studying wine mineral profile has been proven as a valuable tool in geographical origin discrimination and authenticity for both producers and consumers. Adulteration of wines, in terms of geographical origin or variety, is considered a major topic of extensive research. Traceability and authenticity of wines have been previously studied on the basis of typical mineral element patterns by means of chemometric methods. In this context, analytical methods were developed for the determination of mineral elements in wines by inductively coupled plasma–mass spectrometry. This study aimed at classifying selected varietal Greek wines from various regions by employing instrumental analysis. Preliminary data of wine mineral content enabled for the classification of samples according to geographical origin and variety. However, further work is required in order to draw more valid conclusions and to obtain a detailed map of the mineral element content of Greek wines according to their geographical origin and/or variety. [ABSTRACT FROM AUTHOR]

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. Image 1 • Impact of the nanotechnology on wine quality and authentication is presented. • SERS spectroscopy is able to provide vibrational fingerprint of food samples. • Multivariate data analysis is used as a fundamental approach for authentication. • Reached high efficiency in origin and variety discrimination among three white wines. [ABSTRACT FROM AUTHOR]

Highlights • ICP-MS could be an useful tool to classify wines according to multi-element contents. • Dilution/digestion sample treatments and internal/external calibrations were assayed. • The minor and trace elements did not present differences between these four methods. • However, the best method was digestion with internal calibration for major elements. • Sr, Ca, Mg, Mn, Ba, Ni, Cu, Cs, Pb, Zn, and Na were the wine discriminant elements. Abstract Wines from different grape varieties, geographical zones, soil types, foliar N application, SO 2 addition and oak ageing were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). For this purpose, ICP-MS methodology was optimized. The elements which allowed differentiate wines from studied grape varieties were Sr, Ca, Mg and Mn. Geographical zones were classified according to Sr, Ba, Ni, and Cu. Cs and Pb were the main elements to discriminate the wines from the 3 soil types. Wines from several N foliar doses application were classified by Pb, Ni, Mn and Zn. The content of Cs, Mg, Cu and Pb in wines characterized the SO 2 addition. Finally, wines storage in barrels were differentiate by Na and Cs concentration. The discriminant functions classify 100% of the wines, with the exception of grape variety (97.0%) and oak ageing (95.8%). Consequently, ICP-MS can be applied to classified wines according to viticultural and oenological factors. [ABSTRACT FROM AUTHOR]

Geographical product recognition has become an issue for researchers and food industries. One way to obtain useful information about the fingerprint of wines is by examining that fingerprint's chemical components. In this paper, we present a data mining and predictive analysis to classify Brazilian and Uruguayan Tannat wines from the South region using the support vector machine (SVM) classification algorithm with the radial basis kernel function and the F-score feature selection method. A total of 37 Tannat wines differing in geographical origin (9 Brazilian samples and 28 Uruguayan samples) were analyzed. We concluded that given the use of at least one anthocyanin (peon-3-glu) and the radical scavenging activity (DPPH), the Tannat wines can be classified with 94.64% accuracy and 0.90 Matthew's correlation coefficient (MCC). Furthermore, the combination of SVM and feature selection proved useful for determining the main chemical parameters that discriminate with regard to the origin of Tannat wines and classifying them with a high degree of accuracy. Additionally, to our knowledge, this is the first study to classify the Tannat wine variety in the context of two countries in South America. [ABSTRACT FROM AUTHOR]

Amino acids play a relevant role in wine quality and can allow for classifying wines according to the variety. In this work, the amino acid contents of Albariño, Godello, and Treixadura wines, three autochthonous varieties from Galicia (NW Spain), were determined. During three consecutive vintages, these varieties were grown on the same vineyard and were harvested at optimum maturity, and the wines were elaborated following the same enological protocol. The identification and quantification of the primary amino acids were carried out by high-performance liquid chromatography with photodiode array detection, after a derivatization. Amino acid contents in these white varieties were within the range of values reported for other European wines, but Treixadura wines showed the highest concentrations, while wines from the Albariño variety showed the lowest contents. Apart from proline, whose concentrations were caused by yeast release, the most abundant amino acids were aspartic acid, glutamic acid, lysine, arginine, asparagine, alanine, and histidine. Principal component analysis separated wines by variety according to their amino acid contents.

Data mining techniques have been used for the classification of many types of products. In order to classify the Syrah wines from Argentina (Mendoza) and Chile (Central Valley), according to their origin, we perform two feature selection methods with the following classification algorithms: Support Vector Machines (SVM), and two types of artificial neural networks, Multilayer Perceptron (MLP) and Extreme Learning Machine (ELM), on 10-fold cross-validation. Each feature selection method has a different approach, creating also different sets of the most important features. The best model was the combination of variables peon-3-glu, malv-3-glu and pet-3-acetylglu, selected by Random Forest Importance, reaching 98.33% accuracy with ELM, outperforming SVM and MLP. The results obtained from the classifiers and feature subsets are able to confirm the importance of the anthocyanins to classify Syrah wines according to their geographic region. ELM was the best algorithm for classifying Syrah wines. [ABSTRACT FROM AUTHOR]

Geographical product recognition has become an issue for researchers and food industries. One way to obtain useful information about the fingerprint of wines is by examining that fingerprint’s chemical components. In this paper, we present a data mining and predictive analysis to classify Brazilian and Uruguayan Tannat wines from the South region using the support vector machine (SVM) classification algorithm with the radial basis kernel function and the F-score feature selection method. A total of 37 Tannat wines differing in geographical origin (9 Brazilian samples and 28 Uruguayan samples) were analyzed. We concluded that given the use of at least one anthocyanin (peon-3-glu) and the radical scavenging activity (DPPH), the Tannat wines can be classified with 94.64% accuracy and 0.90 Matthew’s correlation coefficient (MCC). Furthermore, the combination of SVM and feature selection proved useful for determining the main chemical parameters that discriminate with regard to the origin of Tannat wines and classifying them with a high degree of accuracy. Additionally, to our knowledge, this is the first study to classify the Tannat wine variety in the context of two countries in South America.

Approaches to the evaluation of generalized wine quality indices based on a set of the quantitative characteristics of single parameters and their organoleptic rating were studied with the use of statistical-probability simulation methods. A general linear model (multiple linear regression) was constructed to predict degustation evaluation values from the concentrations of volatile substances (acetaldehyde, ethyl acetate, methanol, higher alcohols, acetic acid, and furfural) and wine quality classes (high, medium, low, and adulterated). The wine quality class was identified by discriminant analysis based on the concentrations of the above volatile substances. A program module was developed for the automation of a calculation procedure. The average absolute deviation of predicted values from degustation evaluation data in a test sample was 5.8%. [ABSTRACT FROM AUTHOR]

The aim of this work is to propose an alternative way for wine classification and prediction based on an electronic nose (e-nose) combined with Independent Component Analysis (ICA) as a dimensionality reduction technique, Partial Least Squares (PLS) to predict sensorial descriptors and Artificial Neural Networks (ANNs) for classification purpose. A total of 26 wines from different regions, varieties and elaboration processes have been analyzed with an e-nose and tasted by a sensory panel. Successful results have been obtained in most cases for prediction and classification.

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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. [ABSTRACT FROM AUTHOR]

The aim of this work is to propose an alternative way for wine classification and prediction based on an electronic nose (e-nose) combined with Independent Component Analysis (ICA) as a dimensionality reduction technique, Partial Least Squares (PLS) to predict sensorial descriptors and Artificial Neural Networks (ANNs) for classification purpose. A total of 26 wines from different regions, varieties and elaboration processes have been analyzed with an e-nose and tasted by a sensory panel. Successful results have been obtained in most cases for prediction and classification. [ABSTRACT FROM AUTHOR]

Six well established spectrophotometric assays (Folin-Ciocalteu, DPPH, ABTS•+, FRAP, CUPRAC, and the o-diphenols method) have been complementary implemented in order to estimate the total phenolic and o-diphenolic content as well as the free radical scavenging and reducing antioxidant capacities of 40 white and 10 red wines of different varieties and geographic origin. In white wines, the results were weakly correlated; whereas, the results in red wines were strongly correlated, therefore, postulating their separate use in order to estimate efficiently the correlation between results of these methods. The results were subjected to the unsupervised PCA pattern recognition method to investigate the possible classification of white wines. PC analysis framed the wine samples in clear clusters, when the extracted PCA model was based on the results of all six spectrophotometric assays. The usefulness of implementing assays based on different antioxidant mechanisms is discussed. To our knowledge, this is the first time different responses of antioxidants to these spectrophotometric assays have been used to classify white wines according to their variety. [ABSTRACT FROM AUTHOR]

Abstract: A multisensor system combined with multivariate analysis was applied to the characterization of red wines and to the quantification of the grape variety percentage. The proposed system, known as hybrid electronic tongue, consists of a colorimetric optofluidic system and an array of electrochemical sensors. Three monovarietal red wines were studied: Pinot Noir, Merlot and Cabernet Sauvignon. Homemade mixtures were elaborated from these wines according to a Simplex experimental design with 60 samples. The data obtained were treated using advanced chemometric tools like Principal Component Analysis (PCA) and Soft Independent Modeling Class Analogy (SIMCA) for the classification of the wine mixtures and Partial Least Squares (PLS) regression for the quantification of the grape variety composition. The results have shown a good classification of the grape varieties and the identification of the mixtures with Pinot Noir up to 75%. Besides, using the PLS regression, the system has demonstrated a high potential for quantifying the percentage of each grape variety. [Copyright &y& Elsevier]

Abstract: Young and aged wines from two viticole zones in the Andalusian province of Córdoba (southern Spain) were analysed for their content in Ca, Mg, Fe, Cu, Mn and Zn by flame atomic absorption spectrophotometry, and Na, K, Al and Sr by flame atomic emission spectrophotometry. Significant differences in mean content were found for Na, Mn, Mg, Fe and Zn between wines from Montilla–Moriles and Villaviciosa. Linear discriminant analysis using those variables gave 97.9% recognition ability and 95.7% prediction ability. Cluster and principal component analysis show some differences in wines according to geographical origin and to the ageing of wines. Significant differences between young and aged wines were found in the mean content for Mg, K, Sr, Zn and Mn, obtaining 93.62% recognition ability and prediction ability by using linear discriminant analysis and leave-one-out cross-validation test, respectively. Finally, linear discriminant analysis could also be able to classify the samples according to their provenance and to their ageing simultaneously, obtaining 93.6% of the wines correctly classified. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: This work demonstrates the possibility to use artificial neural networks (ANN) for the classification of white varietal wines. A multilayer perceptron technique using quick propagation and quasi-Newton propagation algorithms was the most successful. The developed methodology was applied to classify Slovak white wines of different variety, year of production and from different producers. The wine samples were analysed by the GC–MS technique taking into consideration mainly volatile species, which highly influence the wine aroma (terpenes, esters, alcohols). The analytical data were evaluated by means of the ANN and the classification results were compared with the analysis of variance (ANOVA). A good agreement amongst the applied computational methods has been observed and, in addition, further special information on the importance of the volatile compounds for the wine classification has been provided. [Copyright &y& Elsevier]

This study presents aspects concerning the use of phenolic compounds and chromatic parameters in order to differentiate and classify the red wines obtained by classical maceration-fermentation method, from local grape varieties. 15 wine samples from 'Vulpe', 'Balada', 'Novac', 'Negru de Dragş ani', 'Feteascā neagrà', 'Arcaş ', 'Negru aromat', 'Negru de Cā uş ani', 'Negru vârtos', 'Bãtutà neagra', 'Bā beascà neagrã', 'Negru moale', 'Amurg', 'Codana' and 'Busuioacā de Bohotin' grape variety have been analyzed. The chromatic parameters of the analyzed wine samples were calculated according to CIE Lab 76 methods taking into consideration the absorption spectrum of each wine. A computerized colour simulation with the specialized software Digital Colour Atlas 3.0 was done, underlining the colour differences and thus classifying the wines. Classification of the studied wines according to their colour is similar to the already established order of absorption curves of the same wines. The order established by the values of the phenolic contents is the same with the hierarchy established on anthocyans' content, absorption spectrums and computerized colour simulation. [ABSTRACT FROM AUTHOR]

The results of Chilean wine classification based on the information contained in wine aroma chromatograms measured with a Fast GC Analyzer (zNose™) are reported. The aroma profiles are the results of the derivative of frequency change responses of a surface acoustic wave (SAW) detector when it is exposed to a flux of wine volatile organic compounds (VOCs) during aroma measurement. Classification is done after two sequential procedures: first applying principal component analysis (PCA) or wavelet transform (WT) as feature extraction methods of the aroma data, which results in data dimension reduction. In the second stage, linear discriminant analysis (LDA), radial basis function neural networks (RBFNNs), and support vector machines (SVMs) are used as pattern recognition techniques to perform the classification. This paper compares the performance of three classification methods for three different Chilean wine varieties (Cabernet Sauvignon, Merlot, and Carménère) produced in different years, in different valleys, and by different Chilean vineyards. It is concluded that the highest classification rates were obtained using wavelet decomposition together with SVM with a radial base function (RBF) type of kernel. [ABSTRACT FROM AUTHOR]

Abstract: The feasibility of developing a qualitative model based on cheap and simple instrumentation to differentiate and classify wines from the apellation d’origine “La Mancha” (Spain) has been studied. The criteria for discrimination were origin, grape variety and ageing process. Ultraviolet–visible spectroscopy was used for the development of a inexpensive and simple screening approach. Once spectra were collected, a data exploratory analysis was carried out in order to both show trends hidden in the data matrix from the sample spectra and study the characteristics of the models thus developed. Principal components analysis and soft independent modelling of class analogy were used for the exploratory analysis and the development of classification models, respectively. The ultraviolet region has for the first time been used for the discrimination of types of wines. This region is of great importance for differentiation of wines according to both origin within a same apellation d’origine and ageing process. The sum of false positives and false negatives – the criteria used for evaluating errors – were not higher than 25%, and the classification of wines according to the origin zone yielded the best values (10%). Sample preparation was not necessary. [Copyright &y& Elsevier]

Abstract: In this work, results of Chilean wine classification by means of feature extraction and Bayesian and neural network classification are presented. The classification is made based on the information contained in phenolic compound chromatograms obtained from an HPLC-DAD. The objective of this study is to classify different Cabernet Sauvignon, Merlot and Carménère samples from different years, valleys and vineyards of Chile. Different feature extraction techniques including the discrete Fourier transform, the Wavelet transform, the class profiles and the Fisher transformation are analyzed together with several classification methods such as quadratic discriminant analysis, linear discriminant analysis, K-nearest neighbors and probabilistic neural networks. In order to compare the results, cross validation and re-sampling techniques were used. [Copyright &y& Elsevier]

Abstract: This work presents the results of applying genetic algorithms, in selecting the more relevant features present in chromatograms of polyphenolic compounds, obtained from a high performance liquid chromatograph with aligned photodiodes detector (HPLC-DAD), of samples of Chilean red wines Cabernet Sauvignon, Carmenere and Merlot. From the 6376 points of the original chromatogram, the genetic algorithm is able to select 37 of them, providing better results, from classification point of view, than the case where the complete information is used. The percent of correct classification reached with these 37 features turned out to be 94.19%. [Copyright &y& Elsevier]

Classical multivariate analysis techniques such as factor analysis and stepwise linear discriminant analysis and artificial neural networks method (ANN) have been applied to the classification of Spanish denomination of origin (DO) rose wines according to their geographical origin. Seventy commercial rose wines from four different Spanish DO (Ribera del Duero, Rioja, Valdepen˜as and La Mancha) and two successive vintages were studied. Nineteen different variables were measured in these wines. The stepwise linear discriminant analyses (SLDA) model selected 10 variables obtaining a global percentage of correct classification of 98.8% and of global prediction of 97.3%. The ANN model selected seven variables, five of which were also selected by the SLDA model, and it gave a 100% of correct classification for training and prediction. So, both models can be considered satisfactory and acceptable, being the selected variables useful to classify and differentiate these wines by their origin. Furthermore, the casual index analysis gave information that can be easily explained from an enological point of view. [Copyright &y& Elsevier]

Forty-two Greek white wines from six grape varieties and several geographic regions have been analysed for primary amino acids by reversed-phase high performance liquid chromatography, using precolumn derivatization with o-phthalaldehyde and fluorescence detection. A methodology was developed in order to enable the analysis of a large number of samples, in a quick and reliable way. The amino acid content of Greek white wines was within the range of values reported for other European wines. Wines from Chardonnay, Muscat white and Muscat d''Alexandrie grape varieties, had high amino acid contents, while wines from Asyrtiko, Moschofilero and Debina were characterized by substantially lower amounts of free amino acids. Arginine, γ-amino butyric acid, lysine, alanine, glycine, aspartic acid, and leucine were the most abundant amino acids. In about 29% of the white wine samples examined, the malolactic fermentation has occurred, resulting in lower arginine, γ-amino butyric acid and methionine values for these samples. The amino acid profiles have been useful in the classification of white wines according to grape variety, vintage, geographic origin and type of vinification by means of statistical methods. [Copyright &y& Elsevier]

[Display omitted] • 222 lipid compounds were identified in 48 commercial monovarietal Pinot noir wines. • 58 lipids could be used to as markers to classify wine origin. • Machine learning showed >95% accuracy in identifying wines using lipidomic data. An untargeted lipidomic profiling approach based on ultra - performance liquid chromatography – time-of-flight tandem mass spectrometry (UPLC-TOF-MS/MS) was successfully used to study the origin of commercial Pinot noir wines. The total wine lipids were extracted using a modified Bligh-Dyer method. In all wine samples, the total lipids were less than 0.1% (w/w) of wine. The wines analyzed consisted of 222 lipids from 11 different classes. 48 commercial Pinot noir wine samples were collected from producers in Burgundy, California, Oregon, and New Zealand. Lipidomic data was studied using advanced multivariate analysis methods, random forest, k-nearest neighbor (k-NN), and linear discriminant analysis. The overall classification accuracy was 97.5% for random forest and 90% for k-NN. Wine lipids showed a strong potential for classifying wines by origin, with the top 58 lipids contributing to the discrimination. This information could potentially be used for further study of the impacts of lipids on wine characteristics and authenticity. [ABSTRACT FROM AUTHOR]