Classification of Tea Varieties Using Fuzzy Covariance Learning
Vector Quantization
LI Xiao1, CHEN Yong2, MEI Wu-jun3*, WU Xiao-hong2*, FENG Ya-jie1, WU Bin4
1. Institute of Talented Engineering Students, Jiangsu University, Zhenjiang 212013, China
2. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
3. Research Institute of Zhejiang University-Taizhou, Taizhou 317700, China
4. Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
Abstract：Tea, with much nutrition, is one of the most popular drinks in the world. Good and bad tea are mixed at the market, so it is difficult to make a classification among them. Therefore, using a fast and accurate method to identify tea varieties is meaningful. Most chemical compound’s fundamental frequency absorption bands are within the wavelength range of 2 500~25 000 nm (Mid-infrared region). Large amounts of feature discriminant information in the mid-infrared spectra of tea can be applied to classify tea varieties. This paper proposed a fuzzy covariance the learning vector quantization (FCLVQ) based on the Gustafson-Kessel (GK) clustering. It introduces learning rate of learning vector quantization (LVQ) to control the update rate of cluster centers. Combined with mid-infrared spectroscopy, FCLVQ realizes fast and accurate identification of tea varieties by iteratively calculating the fuzzy membership values and fuzzy clustering centers of samples. Three different kinds of tea(i. e. Emeishan tea, high-quality bamboo-leaf-green tea and low-quality bamboo-leaf-green tea) were selected as 96 samples in total at the market. Each variety corresponds to one group, which consists of 32 samples. The Fourier mid-infrared spectra were collected using an FTIR-7600 spectrometer, and the average spectral data were computed as the final experimental spectra. Firstly, the original spectral data contained noise data, so they were pretreated with multiplicative scattering correction(MSC) to reduce noise. Secondly, principal component analysis(PCA) was employed to reduce the dimensionality of data from 1 868 to 14, and the cumulative contribution of the 14 principal components was 99.74%.Thirdly, the dimensionality of the processed data was reduced to 2, and the discriminant information was extracted by linear discriminant analysis(LDA). Finally, fuzzy C-means clustering(FCM) was run to get initial cluster centers for FCLVQ. The experimental results showed that when the weight index m=2, the accuracy rate of FCLVQ was 95.25%. On the condition of m=2, for the same spectra, the classification accuracy rates of FCM, GK and fuzzy Kohonen clustering(FKCN) were 90.91%, 92.41% and 90.91% respectively. The experimental results showed that compared with the other three algorithms, FCLVQ had a better classification accuracy when m=2 and the number of principal components were 14. Thus, it can be used to classify different tea varieties.
Key words：Mid-infrared spectroscopy; Tea; Fuzzy clustering; Principal component analysis; Linear discriminant analysis
[1] Song X C, Canellas E, Wrona M, et al. Food Packaging and Shelf Life, 2020, 26: 100588.
[2] Yoo S H, Lee Y E, Chung J O, et al. LWT-Food Science and Technology, 2020, 134: 110032.
[3] Lin H Z, Li Z J, Lu H J, et al. Sensors, 2019, 19(21): 4687.
[4] Xu M, Wang J, Zhu L. Journal of Food Science and Technology, 2021, 58(4): 1549.
[5] Gu H, Yin X, Ma Y, et al. LWT-Food Science and Technology, 2020, 130: 109688.
[6] WANG Li, LIN Xin, SHAO Jin-liang, et al(王 丽，林 昕，邵金良，等). Journal of Food Safety and Quality(食品安全质量检测学报), 2020, 11(11): 3586.
[7] Li Y T, Sun J, Wu X H, et al. Journal of Food Science, 2019, 84(8): 2234.
[8] WU Xiao-hong, ZHAI Yan-li, WU Bin, et al(武小红，翟艳丽，武 斌，等). Spectroscopy and Spectral Analysis (光谱学与光谱分析), 2018, 38(06): 1719.
[9] Mishra P, Nordon A, Mohd Asaari M S, et al. Journal of Food Engineering, 2019, 249: 40.
[10] Bakhshipour A, Zareiforoush H, Bagheri I. Journal of Food Measurement and Characterization, 2020, 14(03): 1402.
[11] Sun J, Li Y T, Wu X H, et al. Journal of Food Process Engineering, 2018, 41(8): e12884.
[12] XIE Yu-rong, LI Qiang, WANG Jiao(谢玉荣, 李 强, 王 娇). Journal of Food Safety and Quality(食品安全质量检测学报), 2019, 10(22): 7773.
[13] Adenan M N H, Moosa S, Muhammad S A, et al. Forensic Chemistry, 2020, 17: 100197.
[14] Wei L N, Hu O, Chen H Y, et al. Microchemical Journal, 2020, 158: 105255.
[15] Zhou K L, Yang S L. Pattern Analysis and Applications, 2020, 23: 455.
[16] Wu X, Zhou J, Wu B, et al. Journal of Food Process Engineering, 2019, 42: e13298.
[17] Bodyanskiy Y V, Deineko A O, Eze F M. Automatic Control and Computer Sciences, 2018, 52(3): 166.
