The feasibility of electronic tongue and multivariate analysis was investigated for discriminating the specific geographical origins of a Chinese green tea with Protected Designation of Origin (PDO). 155 Longjing tea samples from three subareas were collected and analyzed by an electronic tongue array of 7 sensors. To remove the influence of abnormal measurements and samples, robust principal component analysis (ROBPCA) was used to detect outliers in each class. Partial least squares discriminant analysis (PLSDA) was then used to develop a classification model. The prediction sensitivity/specificity of PLSDA was 1.000/1.000, 1.000/0.967, and 0.950/1.000 for longjing from Xihu, Qiantang, and Yuezhou, respectively. Electronic tongue and chemometrics can provide a rapid and reliable tool for discriminating the specific producing areas of Longjing. 1. Introduction Green tea, unfermented and made from the leaves of the Camellia sinensis plant, is one of the most popular beverages consumed across the world [1–3]. The property and chemical components of green teas are influenced by many factors, such as tea species, harvest season, climate, geographical locations, and processing. In China, among various factors, the geographical origin is recognized as an important aspect of tea. Because of the similar tea species, cultivation and processing conditions in a specific tea-producing area, many teas are named after their geographical origins. Longjing tea is a green tea produced in Xihu and its surrounding areas (Hangzhou, China). As a famous green tea with Protected Designation of Origin (PDO), Longjing is recognized as one of the top green teas for its special appearance (flat and straight leaves), flavor, and taste. Various methods for distinguishing Longjing from other teas have been reported [4–6]. However, little information has been available on the feasibility of discriminating Longjing from its three specific subproducing areas, namely, Xihu, Qiantang, and Yuezhou. As the quality and prices of Longjing tea from the above three producing areas are different, it is necessary to develop effective analysis methods for discrimination of Longjing from different subproducing areas. Because of the similarity (processing, appearance, and taste) among different subproducing areas, the specific geographical origins of Longjing are usually distinguished by sensory analysis. However, because it is very expensive and may take years to train a tea taster, it would be more efficient to use some nonhuman techniques. Recent years have witnessed increased applications of
References
[1]
L. Xu, D.-H. Deng, and C.-B. Cai, “Predicting the age and type of tuocha tea by fourier transform infrared spectroscopy and chemometric data analysis,” Journal of Agricultural and Food Chemistry, vol. 59, no. 19, pp. 10461–10469, 2011.
[2]
Y.-L. Lin, I.-M. Juan, Y.-L. Chen, Y.-C. Liang, and J.-K. Lin, “Composition of polyphenols in fresh tea leaves and associations of their oxygen-radical-absorbing capacity with antiproliferative actions in fibroblast cells,” Journal of Agricultural and Food Chemistry, vol. 44, no. 6, pp. 1387–1394, 1996.
[3]
T. Karak and R. M. Bhagat, “Trace elements in tea leaves, made tea and tea infusion: a review,” Food Research International, vol. 43, no. 9, pp. 2234–2252, 2010.
[4]
H. Yu, J. Wang, H. Xiao, and M. Liu, “Quality grade identification of green tea using the eigenvalues of PCA based on the E-nose signals,” Sensors and Actuators B, vol. 140, no. 2, pp. 378–382, 2009.
[5]
H. Yu and J. Wang, “Discrimination of LongJing green-tea grade by electronic nose,” Sensors and Actuators B, vol. 122, no. 1, pp. 134–140, 2007.
[6]
K. Wei, L.-Y. Wang, J. Zhou et al., “Comparison of catechins and purine alkaloids in albino and normal green tea cultivars (Camellia sinensis L.) by HPLC,” Food Chemistry, vol. 130, no. 3, pp. 720–724, 2012.
[7]
L. Escuder-Gilabert and M. Peris, “Review: highlights in recent applications of electronic tongues in food analysis,” Analytica Chimica Acta, vol. 665, no. 1, pp. 15–25, 2010.
[8]
L. Sipos, Z. Kovács, V. Sági-Kiss, T. Csiki, Z. Kókai, and A. Fekete, “Discrimination of mineral waters by electronic tongue, sensory evaluation and chemical analysis,” Food Chemistry, vol. 135, no. 4, pp. 2947–2953, 2012.
[9]
L. Gil-Sánchez, J. Soto, R. Martínez-Má？ez, E. Garcia-Breijo, J. Ibá？ez, and E. Llobet, “A novel humid electronic nose combined with an electronic tongue for assessing deterioration of wine,” Sensors and Actuators A, vol. 171, no. 2, pp. 152–158, 2011.
[10]
R. N. Bleibaum, H. Stone, T. Tan, S. Labreche, E. Saint-Martin, and S. Isz, “Comparison of sensory and consumer results with electronic nose and tongue sensors for apple juices,” Food Quality and Preference, vol. 13, no. 6, pp. 409–422, 2002.
[11]
M. Hubert, P. J. Rousseeuw, and S. Verboven, “A fast method for robust principal components with applications to chemometrics,” Chemometrics and Intelligent Laboratory Systems, vol. 60, no. 1-2, pp. 101–111, 2002.
[12]
H.-F. Cui, Z.-H. Ye, L. Xu, X.-S. Fu, C.-W. Fan, and X.-P. Yu, “Automatic and rapid discrimination of cotton genotypes by near infrared spectroscopy and chemometrics,” Journal of Analytical Methods in Chemistry, vol. 2012, Article ID 793468, 7 pages, 2012.
[13]
M. Barker and W. Rayens, “Partial least squares for discrimination,” Journal of Chemometrics, vol. 17, no. 3, pp. 166–173, 2003.
[14]
R. D. Snee, “Validation of regression models: methods and examples,” Technometrics, vol. 19, pp. 415–428, 1977.
[15]
Q.-S. Xu and Y.-Z. Liang, “Monte Carlo cross validation,” Chemometrics and Intelligent Laboratory Systems, vol. 56, no. 1, pp. 1–11, 2001.
