Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM).
Near-infrared (NIR) spectroscopy has been successfully utilized for the rapid identification of green, black and Oolong teas. The spectral features of each category are reasonably differentiated in the NIR region, and the spectral differences provided enough qualitative spectral information for identification. Support vector machine as a pattern recognition was applied to attain the differentiation of the three tea categories in this study. The top five latent variables are extracted by principal component analysis as the input of SVM classifiers. The identification results of the three tea categories were achieved by the RBF SVM classifiers and the polynomial SVM classifiers in different parameters. The best identification accuracies were up to 90%, 100% and 93.33%, respectively, when training, while, 90%, 100% and 95% when test. It was obtained using the RBF SVM classifier with sigma=0.5. The overall results ensure that NIR spectroscopy combined with SVM discrimination method can be efficiently utilized for rapid and simple identification of the different tea categories.
Chen
Quansheng
Q
Department of Food Engineering, School of food and Biological Engineering, Jiangsu University, 212013 Zhenjiang, PR China. chenjiang0518@yahoo.com.cn
Zhao
Jiewen
J
Fang
C H
CH
Wang
Dongmei
D
D005240:Feasibility Studies; D008956:Models, Chemical; D025341:Principal Component Analysis; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D013662:Tea / Q000737:chemistry*
D013662:Tea