Classification of Argentinean Sauvignon Blanc Wines by UV Spectroscopy and Chemometric Methods

Azcarate, Silvana M.; Cantarelli, Miguel Ángel; Pellerano, Roberto Gerardo; Marchevsky, Eduardo Jorge; Camiña, José Manuel

doi:10.1111/1750-3841.12060

Cited by 41 publications

(26 citation statements)

References 24 publications

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“…For these reasons, the elemental composition of different type of wines have been investigated with the aim of correlating them to the provenance soil for geographical tracing purposes (for an extensive review see Versari et al, 2014) ( Table 1 ). However, the critical reading of the scientific literature published in this field of research demonstrates that the determination of the chemical descriptors for the origin of wines are strongly dependent on a plethora of factors, as for instance the number of samples used in the analyses, the type of wine (i.e., white, red, or rosè), the pattern recognition technique applied for the statistical analysis [e.g., Discriminant Analysis, Principal Component Analysis (PCA), Cluster Analysis, Stepwise Linear Discriminant Analysis and similar] and, most importantly, the geographical origin (Baxter et al, 1997; Díaz et al, 2003; Marengo and Aceto, 2003; Castiñeira et al, 2004; Jos et al, 2004; Thiel et al, 2004; Coetzee et al, 2005, 2014; Angus et al, 2006; Capron et al, 2007; Galgano et al, 2008; Serapinas et al, 2008; Forina et al, 2009; Fabani et al, 2010; Catarino et al, 2011; Rodrigues et al, 2011; Martin et al, 2012; Zou et al, 2012; Azcarate et al, 2013; Geana et al, 2013; Šelih et al, 2014). As also shown in Table 1 , the majority of geographical tracing studies explores the analytical dataset by means of unsupervised pattern recognition analyses (e.g., PCA) and, once the most discriminant variables have been found, ad hoc statistical analyses, specifically supervised methods, are run in order to exacerbate the clusterization and to extract further information from the dataset.…”

Section: Traceability Of Winesmentioning

confidence: 99%

Prospect on Ionomic Signatures for the Classification of Grapevine Berries According to Their Geographical Origin

et al. 2017

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The determination of food geographical origin has been an important subject of study over the past decade, with an increasing number of analytical techniques being developed to determine the provenance of agricultural products. Agricultural soils can differ for the composition and the relative quantities of mineral nutrients and trace elements whose bioavailability depends on soil properties. Therefore, the ionome of fruits, vegetables and derived products can reflect the mineral composition of the growth substrate. Multi-elemental analysis has been successfully applied to trace the provenance of wines from different countries or different wine-producing regions. However, winemaking process and environmental and cultural conditions may affect a geographical fingerprint. In this article, we discuss the possibility of applying ionomics in wines classification on a local scale and also by exploiting grape berry analyses. In this regard, we present the ionomic profile of grapevine berries grown within an area of approximately 300 km2 and the subsequent application of chemometric methods for the assignment of their geographical origin. The best discrimination was obtained by using a dataset composed only of rare earth elements. Considering the experiences reported in the literature and our results, we concluded that sample representativeness and the application of a preliminary Principal Component Analysis, as pattern recognition techniques, might represent two necessary starting points for the geographical determination of the geographical origin of grape berries; therefore, on the basis of these observations we also include some recommendations to be considered for future application of these techniques for grape and wines classification.

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Section: Traceability Of Winesmentioning

confidence: 99%

Prospect on Ionomic Signatures for the Classification of Grapevine Berries According to Their Geographical Origin

et al. 2017

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“…The FT‐IR data represent a set of multiple variables which contain overlapping information. Principal component analysis (PCA) is often used as the 1st step to extract and visualize the main information in spectra data, eliminate much overlapping information, and reduce the dimension of data (Azcarate and others ). In this study, a preliminary analysis of data was performed by PCA to examine the potential of grouping samples according to the manufacture wineries (brands).…”

Section: Resultsmentioning

confidence: 99%

Use of Attenuated Total Reflectance Mid‐Infrared Spectroscopy for Rapid Prediction of Amino Acids in Chinese Rice Wine

Long

et al. 2015

Journal of Food Science

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The high content of amino acids of Chinese rice wine (CRW), especially essential amino acids makes it a food increasingly demanded by consumers. Rapid detection technique of amino acid content, which is an important quality and function index of CRW, is highly desirable for consumers, producers as well as administrative authorities. In this study, the potential of Fourier transform infrared spectroscopy (FT-IR) as a novel and rapid analytical technique to determine 17 free amino acids in CRW were investigated. Genetic algorithms (GA) and synergy interval partial least squares (SiPLS) were used to select the most efficient spectral variables to improve the prediction precision of the classic partial least squares (PLS) model constructed on the full-spectrum. The results demonstrated that compared with the PLS model using all wavelengths of FT-IR spectra, the prediction precision of model based on the spectral variables selected by GA and SiPLS was significantly improved, especially for arginine and proline. After systemic comparison and discussion, it was found that GA-SiPLS model achieved the best performance, with the correlation coefficient in calibration (R(2) (cal)) higher than 0.80 and the residual predictive deviation higher than 2.00 for all of the free amino acids analyzed in this study. The overall results confirmed that FT-IR combined with efficient variable selection algorithms is a method that may be useful to replace the traditional methods for routine analysis of free amino acids in CRW.

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“…However multidimensional GC/MS method is relatively expensive, time-consuming and requires highly skilled operators. Up to date, several simple and rapid methods such as ultraviolet (UV), visible (VIS), infrared (IR)and fluorescence spectroscopies have been tried for determining the origin of beverages (Shen et al 2012;Azcarate et al 2013;MarteloVidal et al 2013). The last technique is particularly attractive because of its high sensitivity and excellent specificity.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing between juniper-flavoured spirit drinks from different producers

Uríčková¹,

Dobóová²,

Sádecká³

2017

Nova Biotechnologica Et Chimica

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A simple method for classifying juniper-flavoured spirit drinks is proposed based on the ratio of fluorescence intensity values in synchronous fluorescence spectra. Receiver operating curves (ROC) and linear discriminant analysis (LDA) were used to compute the performance of the classification. Significant differences in the fluorescence intensity ratios (I 316 /I 287 and I 324 /I 287 ) observed in the spectra recorded using wavelength difference 10 nm were evaluated by ROC analysis to identify cutoff values that gave ideal AUCs equal to one, thus allowing for 100% correct classification of the samples according to producer criteria. LDA showed that drinks of different producers could be distinguished (100% correct classification) on the basis of their differences in the fluorescence intensity ratios (I 323 /I 287 , I 324 /I 287 , I 316 /I 287 and I 325 /I 287 ). These results show that complete synchronous spectra are not required to discriminate between producers. Instead of them, fluorescence intensity could be measured at selected wavelengths.

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Classification of Argentinean Sauvignon Blanc Wines by UV Spectroscopy and Chemometric Methods

Abstract: This manuscript describes a method to determine the geographical origin of Sauvignon wines from Argentina. The main advantage of this method is the use of nonexpensive techniques, such as UV-Vis spectroscopy.

Cited by 41 publications

References 24 publications

Prospect on Ionomic Signatures for the Classification of Grapevine Berries According to Their Geographical Origin

Prospect on Ionomic Signatures for the Classification of Grapevine Berries According to Their Geographical Origin

Use of Attenuated Total Reflectance Mid‐Infrared Spectroscopy for Rapid Prediction of Amino Acids in Chinese Rice Wine

Distinguishing between juniper-flavoured spirit drinks from different producers

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