Discrimination of Tea Varieties by Using Infrared Spectroscopy with a Novel Generalized Noise Clustering
WU Bin1, CUI Yan-hai2, WU Xiao-hong3*, JIA Hong-wen1, LI Min4
1. Department of Information Engineering, Chuzhou Vocational Technology College, Chuzhou 239000, China 2. Jingjiang College, Jiangsu University, Zhenjiang 212013, China 3. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China 4. School of Physics and Electronic Engineering of Leshan Normal University, Leshan 614000, China
Abstract：The discrimination of tea varieties plays very important role in the production and sale of tea. It is of great significance for the study of a fast, easy and simple method for the identification of tea varieties. The combination of infrared spectroscopy detection technology and fuzzy clustering algorithm is one of the most effective and practical techniques in the detection of tea varieties. To realize the rapid discrimination of tea varieties, a novel generalized noise clustering (NGNC) was proposed based on fast generalized noise clustering (FGNC). Euclidean distance in the objective function of FGNC was replaced with the pth power of Euclidean distance and clustering accuracy was being improved. Emeishan Maofeng; high quality Leshan trimeresurus and low quality Leshan trimeresurus were prepared as the research object and the infrared reflectance (IR) spectra of tea samples were collected with FTIR-7600 infrared spectrometer. Firstly, the high-dimensional IR spectra of tea samples were reduced by principal component analysis (PCA). Secondly, linear discriminant analysis (LDA) was used to extract the discriminant information from the low-dimensional data. Finally, FGNC and NGNC were performed to identify tea varieties. The experimental results showed that in comparision with FGNC, NGNC has higher clustering accuracy, better cluster centers and faster convergence speed. Infrared spectroscopy coupled with NGNC, PCA and LDA could cluster IR spectra of tea samples quickly and correctly, which provided a new method and new idea for identifying tea varieties based on infrared spectroscopy and fuzzy clustering.
[1] Chen Q, Zhang D, Pan W, et al. Trends in Food Science & Technology, 2015, 43(1): 63. [2] ZHANG Long, PAN Jia-rong, ZHU Cheng(张 龙，潘家荣，朱 诚). Food Science(食品科学), 2012, 33(20): 149. [3] NING Jing-ming, WAN Xiao-chun, ZHANG Zheng-zhu, et al(宁井铭，宛晓春，张正竹, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(11): 255. [4] Huang X, Zou X, Zhao J, et al. Food Chemistry, 2014, 164: 536. [5] Ren G, Wang S, Ning J, et al. Food Research International, 2013, 53(2): 822. [6] He W, Zhou J, Cheng H, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2012, 86: 399. [7] Xiong C, Liu C, Pan W, et al. Food Chemistry, 2015, 176(1): 130. [8] Kannan S R, Devi R, Ramathilagam S, et al. Computers in Biology and Medicine, 2013, 43(2)：73. [9] HE Guang-pu, LI Min, WU Bin, et al(何光普，李 敏，武 斌，等). Journal of Beijing Jiaotong University(北京交通大学学报), 2008, 32(6): 98. [10] WU Bin, WU Xiao-hong, JIA Hong-wen(武 斌，武小红，贾红雯). Computer Engineering and Applications(计算机工程与应用), 2013, 49(13): 145. [11] Sirisomboon P, Tanaka M, Kojima T, et al. Journal of Food Engineering, 2012, 112(3): 218. [12] Brito A L B, Brito L R, Honorato F A, et al. Food Research International, 2013, 51(2): 924. [13] Wu X H, Wu B, Sun J, et al. Applied Mathematical Modelling, 2011, 35: 4790.
