Discrimination between Shiraz Wines from Different Australian Regions: The Role of Spectroscopy and Chemometrics

Abstract

This study reports the use of UV-visible (UV-vis), near-infrared (NIR), and midinfrared (MIR) spectroscopy combined with chemometrics to discriminate among Shiraz wines produced in five Australian regions. In total, 98 commercial Shiraz samples (vintage 2006) were analyzed using UV-vis, NIR, and MIR wavelength regions. Spectral data were interpreted using principal component analysis (PCA), linear discriminant analysis (LDA), and soft independent model of class analogy (SIMCA) to classify the wine samples according to region. The results indicated that wine samples from Western Australia and Coonawarra can be separated from the other wines based on their MIR spectra. Classification results based on MIR spectra also indicated that LDA achieved 73% overall correct classification, while SIMCA 95.3%. This study demonstrated that IR spectroscopy combined with chemometric methods can be a useful tool for wine region discrimination.

An analytical approach to Sr isotope ratio determination in Lambrusco wines for geographical traceability purposes

“…Actually, there are different European rules (Regulation (EC), 1898,2002,2006,2008) with the aim to regulate and guarantee the authenticity and geographical traceability of food, but unfortunately, these are often not enough to avoid frauds or false labelling declarations, and to assure an honest marketing competiveness. Hence, several researches have been addressed to the development of analytical methodologies, able to link the product to its territory of origin by means of different parameters such as inorganic/organic chemical patterns and isotopic signature (Adami et al, 2010;Almeida & Vasconcelos, 2003;Di PaolaNaranjo et al, 2011;Durante et al, 2013;Laaks, Letzel, Schmidt, & Jochmann, 2012;Riovanto, Cynkar, Berzaghi, & Cozzolino, 2011). Among the different indicators, 87 Sr/ 86 Sr isotopic ratio, Sr-I.R., has provided excellent results for different types of food matrices (Asfaha et al, 2011;Baroni et al, 2011;Swoboda et al, 2008;Voerkelius et al, 2010), and in particular for wine (Almeida & Vasconcelos, 2004;Barbaste, Robinson, Guilfoyle, Medina, & Lobinski, 2002;Di Paola-Naranjo et al, 2011;Durante et al, 2013;Marchionni et al, 2013).…”

Rice Seed Cultivar Identification Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis

“…After spectra or hyperspectral image acquisition, data analysis has a direct effect on the performance. Principal Component Analysis (PCA) [15], Partial Least Squares Discriminant Analysis (PLS-DA) [16], Soft Independent Modeling of Class Analogy (SIMCA) [17], Linear Discriminant Analysis (LDA) [18], K-Nearest Neighbor Algorithm (KNN) [19], Artificial Neural Network (ANN) [20], Support Vector Machine (SVM) [21] and Least-squares Support Vector Machine (LS-SVM) [22] have all been used to deal with classification issues, and these methods have proved to be effective. In recent years, modern applied mathematics has offered some new methods.…”

Evaluation of ultraviolet, visible, and near infrared spectroscopy for the analysis of wine compounds

“…Compared to traditional methods, multivariate data analysis combined with modern UV-VIS-NIR instrumental techniques (Urbano-Cuadrado et al 2005;Riovanto et al 2011) gives a new and a better insight into complex problems by measuring a great number of chemical compounds at once, thus enabling the fingerprinting of each sample. These methods are attractive due to their inherent features of versatility, flexibility, effectiveness, and richness of information (Cozzolino et al 2011c).…”