Geographic Classification of Spanish and Australian Tempranillo Red Wines by Visible and Near-Infrared Spectroscopy Combined with Multivariate Analysis L. LIU, †,‡ D. COZZOLINO,* ,‡ W. U. CYNKAR, ‡ M. GISHEN, ‡ AND C. B. COLBY † School of Chemical Engineering, Engineering North Building, The University of Adelaide, Adelaide SA 5005, Australia, The Australian Wine Research Institute, Waite Road, Urrbrae. P.O. Box 197, Adelaide SA 5064, Australia, and Cooperative Research Centre for Viticulture, P.O. Box 154, Adelaide SA 5064, Australia Visible (vis) and near-infrared (NIR) spectroscopy combined with multivariate analysis was used to classify the geographical origin of commercial Tempranillo wines from Australia and Spain. Wines (n ) 63) were scanned in the vis and NIR regions (400-2500 nm) in a monochromator instrument in transmission. Principal component analysis (PCA), discriminant partial least-squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) based on PCA scores were used to classify Tempranillo wines according to their geographical origin. Full cross-validation (leave-one-out) was used as validation method when PCA and LDA classification models were developed. PLS-DA models correctly classified 100% and 84.7% of the Australian and Spanish Tempranillo wine samples, respectively. LDA calibration models correctly classified 72% of the Australian wines and 85% of the Spanish wines. These results demonstrate the potential use of vis and NIR spectroscopy, combined with chemometrics as a rapid method to classify Tempranillo wines accordingly to their geographical origin. KEYWORDS: Near-infrared; principal component analysis; discriminant partial least-squares; linear discriminant analysis; Tempranillo; wine; geographical origin INTRODUCTION Wine has become a commodity of significant commercial value, and consumers expectations depend on many factors, such as grape variety and maturity, geographic origin, and vinification techniques (1). In most wine producing countries in Europe, wine quality value is associated with both climate and soil characteristics, in particular defined by geographical classifica- tion or denomination of origin systems (2-4). Today, the determination of food authenticity and the detection of adultera- tion are major issues in the food industry and are attracting an increasing amount of attention for wine producers, researchers, and consumers (3). Wine quality is related to an obvious commercial value, determining that adulteration is possible to be practiced, which may bring an unfair competition in the wine industry and harm the rights of consumers (2, 3). Thus, there is significant interest in accurate methods for wine characterization that could be used to prevent adulteration. Current research has primarily focused on wine classification according to geographical origin, using sophisticated and expensive analytical equipment such as high performance liquid chromatography, inductively coupled plasma spectrometry, gas chromatography, and atomic absorption spectroscopy (2, 3). Additionally, the use of multivariate statistical techniques (chemometrics) on chemical and sensory data has gained increasing attention as a tool to classify wines from different geographical regions and to describe similar sensory and chemical characteristics. A diverse range of physicochemical parameters have been measured in wines to classify samples according to geographic origin, such as phenolic compounds (5, 12), macro- and trace elements (6, 7, 10, 11), physical and chemical characteristics (8-10), amino acids and biogenic amines (13), and volatile compounds (4, 14). Although these methods provide valuable information, most of them involve time-consuming, laborious, and costly procedures. Near-infrared (NIR) spectroscopy has been used to quanti- tatively predict the concentration of various constituents in food and agricultural products, including wine (15-16). NIR spec- troscopy is commonly used by the wine industry to monitor fruit quality and to determine the concentration of several chemical parameters in wine using commercially available instruments (17, 18). One advantage of NIR spectroscopy is that it can record the response of the molecular bonds of its chemical constituents to the NIR spectrum (e.g., O-H, N-H, and C-H bonds) and thereby build a characteristic spectrum that behaves as a fingerprint of the sample (15, 16). It is well- * Corresponding author. E-mail: daniel.cozzolino@awri.com.au. Fax: + 61 8 8303 6601. † The University of Adelaide. ‡ The Australian Wine Research Institute and Cooperative Research Centre for Viticulture. 6754 J. Agric. Food Chem. 2006, 54, 6754-6759 10.1021/jf061528b CCC: $33.50 © 2006 American Chemical Society Published on Web 08/12/2006