The Identification of Lettuce Varieties by Using Unsupervised Possibilistic Fuzzy Learning Vector Quantization and Near Infrared Spectroscopy
WU Xiao-hong1, 2, CAI Pei-qiang3, WU Bin4, SUN Jun1, 2, JI Gang1
1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China 2. Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry, Jiangsu University, Zhenjiang 212013, China 3. Jingjiang College, Jiangsu University, Zhenjiang 212013, China 4. Department of Information Engineering, Chuzhou Vocational Technology College, Chuzhou 239000, China
Abstract：To solve the noisy sensitivity problem of fuzzy learning vector quantization (FLVQ), unsupervised possibilistic fuzzy learning vector quantization (UPFLVQ) was proposed based on unsupervised possibilistic fuzzy clustering (UPFC). UPFLVQ aimed to use fuzzy membership values and typicality values of UPFC to update the learning rate of learning vector quantization network and cluster centers. UPFLVQ is an unsupervised machine learning algorithm and it can be applied to classify without learning samples. UPFLVQ was used in the identification of lettuce varieties by near infrared spectroscopy (NIS). Short wave and long wave near infrared spectra of three types of lettuces were collected by FieldSpec@3 portable spectrometer in the wavelength range of 350～2 500 nm. When the near infrared spectra were compressed by principal component analysis (PCA), the first three principal components explained 97.50% of the total variance in near infrared spectra. After fuzzy c-means (FCM) clustering was performed for its cluster centers as the initial cluster centers of UPFLVQ, UPFLVQ could classify lettuce varieties with the terminal fuzzy membership values and typicality values. The experimental results showed that UPFLVQ together with NIS provided an effective method of identification of lettuce varieties with advantages such as fast testing, high accuracy rate and non-destructive characteristics. UPFLVQ is a clustering algorithm by combining UPFC and FLVQ, and it need not prepare any learning samples for the identification of lettuce varieties by NIS. UPFLVQ is suitable for linear separable data clustering and it provides a novel method for fast and nondestructive identification of lettuce varieties.
Key words：Near infrared spectroscopy;Lettuce;Identification of varieties;Unsupervised machine learning
[1] SUN Jun, JIN Xia-ming, MAO Han-ping, et al(孙 俊，金夏明，毛罕平，等). Chinese Journal of Analytical Chemistry(分析化学), 2014, 42(5): 672. [2] Ahmed M R, Daniel E G, William K, et al. Journal of Food Engineering, 2014, 135: 11. [3] Schmutzler M, Huck C W. Vibrational Spectroscopy, 2014, 72: 97. [4] Ferreira D S, Pallone J A L, Poppi R J. Food Control, 2015, 48: 91. [5] Fernández Pierna J A, Vermeulen P, Amand O, et al. Chemometrics and Intelligent Laboratory Systems, 2012, 117: 233. [6] Xu L, Shi P T, Ye Z H, et al. Food Chemistry, 2013, 141: 2434. [7] Tsao E C, Bezdek J C, Pal N R. Pattern Recognition, 1994, 27(5): 757. [8] Krishnapuram R, Keller J. IEEE Transaction on Fuzzy Systems, 1993, 1(2): 98. [9] Wu X, Fu H, Wu B, et al. Journal of Information and Computational Science, 2010，7(3): 777. [10] Shen F, Wu J, Ying Y B, et al. Food Chemistry, 2013, 141(4): 4026. [11] Pholpho T, Pathaveerat S, Sirisomboon P. Journal of Food Engineering, 2011, 104(1): 169. [12] Milton C S B, Ma I G, Javier S E, et al. Talanta, 2013, 116: 50. [13] Shi J Y, Zou X B, Huang X W, et al. Food Chemistry, 2013, 138: 192. [14] Luo W, Huan S, Fu H, et al. Food Chemistry, 2011, 128(2): 555. [15] Wu X, Wu B, Sun J, et al. Journal of Information and Computational Science, 2010, 7(5): 1075.
