Ultraviolet–visible spectroscopy and pattern recognition methods for differentiation and classification of wines

Urbano, M.; Castro, M.D. Luque de; Pérez, Pedro María; García-Olmo, Juan; Gómez‐Nieto, Miguel Ángel

doi:10.1016/j.foodchem.2005.05.001

Cited by 115 publications

(70 citation statements)

References 23 publications

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“…These studies generally achieve very accurate results although they are destructive and time consuming analyses that also require the use of chemical reagents. Other authors have classified wine and grape samples using spectroscopic tools in the ultraviolet-visible and near-infrared regions in order to discriminate samples according to their grape variety and origin [11][12][13]. Urbano et al [11] use ultraviolet-visible spectroscopy in order to discriminate wine samples according to their origin, grape variety and ageing process.…”

Section: Introductionmentioning

confidence: 99%

Comparative study on the use of anthocyanin profile, color image analysis and near-infrared hyperspectral imaging as tools to discriminate between four autochthonous red grape cultivars from La Rioja (Spain)

Nogales-Bueno

Rodríguez-Pulido

Heredia

et al. 2015

Talanta

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a b s t r a c tThree 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.

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Section: Introductionmentioning

confidence: 99%

Comparative study on the use of anthocyanin profile, color image analysis and near-infrared hyperspectral imaging as tools to discriminate between four autochthonous red grape cultivars from La Rioja (Spain)

Nogales-Bueno

Rodríguez-Pulido

Heredia

et al. 2015

Talanta

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“…22 Pelo exposto, uma boa alternativa para a realização rápida de uma análise screening de vinhos é a combinação da espectrometria UV-VIS associada ao uso de analisadores automáticos em fluxo; entretanto, esta metodologia gera sinais complexos do ponto de vista matemático sendo então necessária a utilização da quimiometria. [23][24][25][26][27][28][29][30][31][32][33][34] O uso da quimiometria tem como objetivo tratar dados de caráter multivariado retirando o máximo de informações presentes em suas matrizes complexas, facilitando assim a interpretação dos mesmos. Diferentes ferramentas quimiométricas são utilizadas, tais como, análise de agrupamento hierárquica (HCA -Hierachical Cluster Analysis), análise em componentes principais (PCA -Principal Component Analysis), modelagem independente flexível por analogia de classe (SIMCA -Soft Independent Modelling of Class Analogy), dentre outras.…”

Section: Introductionunclassified

“…[23][24][25][26][27][28][29][30][31][32][33][34] O uso da quimiometria tem como objetivo tratar dados de caráter multivariado retirando o máximo de informações presentes em suas matrizes complexas, facilitando assim a interpretação dos mesmos. Diferentes ferramentas quimiométricas são utilizadas, tais como, análise de agrupamento…”

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Análise screening de vinhos empregando um analisador fluxo-batelada, espectroscopia UV-VIS e quimiometria

et al. 2010

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Recebido em 12/3/09; aceito em 13/8/09; publicado na web em 8/1/10 SCREENING ANALYSIS OF WINES USING FLOW-BATCH ANALYZER, UV-VIS SPECTROSCOPY AND CHEMOMETRICS. A simple, robust, versatile, high analytical frequency method was proposed to check if a sample of wine is within the range of standards set by the manufacturer, using the UV-VIS spectroscopy, multivariate analysis and a flow-batch analyzer. Two hundred and fifty-two samples of wines were analyzed. The results from the application of Hierachical Cluster Analysis (HCA) to the matrix of the data involving all samples show the formation of fifteen types of wine. A Soft Independent Modelling of Class Analogy (SIMCA) model was constructed and used to classify the samples of the overall forecast. As a result, it is observed that the prediction was performed with a success rate of 99.2% for a confidence level of 95%. This shows that the proposed methodology can be used as an effective tool for classifying of samples of wines.Keywords: quality control of wines; flow-batch analyzer; chemometrics. INTRODUÇÃOO vinho é uma bebida simples, resultante da conversão gradual dos açúcares presentes no mosto de uvas frescas em álcool, CO 2 e energia por intermédio do levedo.1 Desta forma, vinhos de má qualidade são facilmente elaborados, bem como bastante propícios a falsificações e/ou adulterações. As falsificações e/ou adulterações de alimentos constituem crime contra a saúde pública, na medida em que são comercializados produtos ilegais, sem controle e sem fiscalização, que podem prejudicar a saúde da população, além de lesar o consumidor que paga caro por um produto de qualidade e de sonegar impostos aos órgãos governamentais. Muitos são os relatos de casos de falsificação e/ou adulterações de vinhos e, preocupadas com este problema, as indústrias produtoras tentam, a todo custo, tomar medidas que amenizem esta situação.Existe uma vasta variedade de vinhos e a forma mais comum para classificá-los corresponde ao seu aspecto visual e pigmentação característica. Porém, existem outras classificações que têm a ver com o processo de vinificação ou, ainda, quanto ao teor de açúcar. A classificação e a caracterização de vinhos vêm sendo realizadas através da utilização de alguns parâmetros como teor alcoólico, pH, acidez fixa e volátil, teor de cinzas etc.2 Para isso têm sido utilizados métodos espectroscópicos de massa, 3 de emissão óptica, 4 de espectroscopia NIR 5 e de absorção atômica em chama. 6 No entanto, a técnica analítica padrão de referência utilizada para a verificação da autenticidade de vinhos no Brasil é a cromatografia.7 Tal técnica apresenta um custo elevado de seus instrumentos e de manutenção constante, bem como requer a preparação de reagentes e soluções padrão com produção de resíduos prejudiciais à saúde e/ou ao meio ambiente, além de apresentar uma baixa frequência analítica. Uma boa alternativa para superar estes inconvenientes é o desenvolvimento de métodos analíticos que combinam o uso da espectrometria de absorção molecular UV-VIS, analisadores automá...

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“…Wines have so far been classified in terms of vintage, origin and other properties on the basis of various compounds and compound families including elements and isotopes [2], terpenoid profile [3], polyphenols [4,5] and the global volatile fingerprint [6] as determined by UV-Vis [7], infrared [8,9] and fluorescence spectroscopy [10], mass spectrometry [11,12] or nuclear magnetic resonance spectroscopy [13]. In this context, conventional methods have been supplemented with electronic tongues, noses and other sensor array devices for wine-monitoring purposes [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Differentiation and identification of white wine varieties by using electropherogram fingerprints obtained with CE

Garrido-Delgado

López-Vidal

Arce

et al. 2009

J of Separation Science

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This article reports a method for determining organic acids by CE in order to obtain a characteristic fingerprint with a view to classifying white wine samples with the aid of multivariate statistics. The proposed method was optimized for the determination of the most abundant organic acids in wine (viz. succinic, malic, tartaric, citric, acetic and lactic). The acids were separated in a running buffer consisting of 180 mM disodium hydrogen phosphate, 0.5 mM CTAB and 10% methanol, adjusted to pH 7.5. The applied voltage was 10 kV and the working temperature 251C. The precision of the method was assessed by replicate analysis of a wine sample; the resulting RSD was less than 5.2% in peak area and less than 1% in migration time for all organic acids. Calibration curves exhibited good linearity throughout the studied concentration range for all acids. Principal component analysis and linear discriminant analysis allowed accurate classification of wine samples with variable alcohol contents from their electrophoretic fingerprints. The discriminant potential of various electropherogram regions was examined. Based on the results, the proposed method is an effective choice for classifying wine samples. IntroductionThe winemaking sector is one of the main motors of the economy in Spain. Spanish white wines are high-quality products much appreciated by consumers inside the country and also, increasingly, abroad [1]. Therefore, being able to confirm their authenticity is of paramount importance with a view to assuring their quality to an expanding international market. Multivariate analysis has been widely used for this purpose in recent years.Wines [11,12] or nuclear magnetic resonance spectroscopy [13]. In this context, conventional methods have been supplemented with electronic tongues, noses and other sensor array devices for wine-monitoring purposes [14][15][16][17]. These instrumental techniques have been used in combination with chemometric tools such as artificial neural networks in order to classify white wines [18]. Separation techniques such as CE have also been used to classify wines [19][20][21][22]. To our knowledge, electropherogram profiles, which are constructed from the CE response of organic acids, have never, to date, been used as fingerprints for white wines and their classification with chemometric models. In this study, we used electropherogram fingerprints obtained with CE to classify white wines for the first time.Organic acids present in grape juices and wines are important because they influence the sensory properties (flavour, colour and aroma), and the physical and microbiological stability, of the product. Acetic, lactic, succinic, malic, citric and tartaric are the most abundant organic acids in wines. Tartaric, malic and citric acid are obtained directly from the grape, whereas succinic, lactic and acetic acid form during the fermentation process. These organic acids are also very useful for detecting wine alteration and/ or disease [23].Malic acid is present in large amounts in green gra...

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Ultraviolet–visible spectroscopy and pattern recognition methods for differentiation and classification of wines

Cited by 115 publications

References 23 publications

Comparative study on the use of anthocyanin profile, color image analysis and near-infrared hyperspectral imaging as tools to discriminate between four autochthonous red grape cultivars from La Rioja (Spain)

Comparative study on the use of anthocyanin profile, color image analysis and near-infrared hyperspectral imaging as tools to discriminate between four autochthonous red grape cultivars from La Rioja (Spain)

Análise screening de vinhos empregando um analisador fluxo-batelada, espectroscopia UV-VIS e quimiometria

Differentiation and identification of white wine varieties by using electropherogram fingerprints obtained with CE

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