Mixed Fuzzy Maximum Entropy Clustering Analysis of FT-NIR Spectra of Tea
FU Hai-jun1, 2, ZHOU Shu-bin1, 3, WU Xiao-hong1, 2*, WU Bin4, SUN Jun1, 2, DAI Chun-xia1, 5
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. Institute of Scientific and Technical Information, Jiangsu University, Zhenjiang 212013, China
4. Department of Information Engineering, Chuzhou Vocational Technology College, Chuzhou 239000, China
5. School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Abstract：Tea is one of the three most popular drinks in the world. It can not only refresh the mind, but also help digestion and lower blood pressure. With the increasing advance of requirements of tea quality by people, it is necessary to achieve accurate identification of different varieties of tea to prevent the false tea brands and adulteration in the tea market from happening. In order to identify tea varieties quickly and accurately, a tea variety identification system was designed with a combination of Fourier transform near-infrared spectroscopy (FT-NIR) and a novel fuzzy maximum entropy clustering. When traditional fuzzy maximum entropy clustering (FEC) clusters the data with noise, clustering results are often prone to errors, that is to say, FEC is sensitive to noise. To solve this problem, a mixed fuzzy maximum entropy clustering (MFEC) was proposed by introducing possibilistic c-means (PCM)clustering into traditional FEC. MFEC has fuzzy membership and typicality values by iterative computing, and it can cluster FT-NIR data mixed with noise accurately. Firstly, three kinds of Anhui tea samples (i. e. Yuexi Cuilan, Lu’an Guapian and Shiji Maofeng) were prepared for FT-NIR data collection with Antaris Ⅱ spectrometer in the wave number range of 10 000～4 000 cm-1. Secondly, spectral data were preprocessed by multiple scattering correction (MSC), and then the dimensionality of the data was reduced to 10 by principal component analysis (PCA), and then the discriminant information of the data was extracted by linear discriminant analysis (LDA). Finally, MFEC and FEC were applied to perform clustering analysis on the data, respectively, and they were compared in the clustering accuracy and convergence speed. The results of this study indicated that in the condition of m=2, the clustering accuracy rate of MFEC was 100%, while that of FEC was 37.98%. MFEC achieved convergence after four iterations while FEC converged after 100 iterations. Therefore, MFEC could cluster spectral data more efficiently than FEC, and MFEC had the obvious superiority. Three types of Anhui tea samples could be classified correctly and efficiently by combining FT-NIR technology with PCA, LDA and MFEC. This method provided an innovative method and design idea for the identification analysis in the tea testing field, and it has certain theoretical value and good market application prospect.
Key words：Near-infrared spectroscopy; Tea; Principal component analysis; Linear discriminant analysis; Fuzzy maximum entropy clustering
[1] Wu X H, Wu B, Sun J, et al. International Journal of Food Properties, 2016, 19: 1016.
[2] 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(6): 1719.
[3] Barbin D F, Sun D W, Nixdorf S L, et al. Food Research International, 2014, 61(7): 23.
[4] Cebi N, Yilmaz M T, Sagdic O. Food Chemistry, 2017, 229: 517.
[5] Zhuang X G, Wang L L, Chen Q, et al. Science China Technological Sciences, 2017, 60(1): 84.
[6] Cai J X, Wang Y F, Xi X G, et al. International Journal of Biological Macromolecules, 2015, 78: 439.
[7] Wu X H, Zhu J, Wu B, et al. Computers & Electronics in Agriculture, 2018, 147: 64.
[8] Deng S, Xu Y, Li X, et al. Computers & Electronics in Agriculture, 2015, 118(C): 38.
[9] Li X, Sun C, Luo L, et al. Computers & Electronics in Agriculture, 2015, 112: 28.
[10] Xiong C, Liu C, Pan W, et al. Food Chemistry, 2015, 176: 130.
[11] Xu L, Fu X, Fu H, et al. Journal of Food Quality, 2016, 38(6): 450.
[12] Li C, Guo H, Zong B, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy，2019，206: 254.
[13] Jaiswal P, Jha S N, Kaur J, et al. Food Chemistry, 2018, 238: 209.
[14] Salman A, Shufan E, Sahu R K, et al. Vibrational Spectroscopy, 2016, 83: 17.
[15] Li R P, Mukaidono M. Fuzzy Sets and Systems, 1999, 102(2): 253.
[16] Krishnapuram R, Keller J. IEEE Transactions on Fuzzy Systems, 1993, 1(2): 98.
