Possibilistic Fuzzy K-Harmonic Means Clustering of Fourier Transform Infrared Spectra of Tea
WU Bin1, WANG Da-zhi2, WU Xiao-hong3, 4*, JIA Hong-wen1
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. Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry, Jiangsu University, Zhenjiang 212013, China
Abstract：Different variety of tea often has diversified organic chemical components, and their effects are not the same. Therefore, it is very necessary to develop a simple, efficient, high recognition rate method in classifying tea varieties. Mid-infrared spectroscopy is a rapid detection technology, and there is noise signal in the mid-infrared spectra of tea samples collected by spectrometer. With a view to identifying tea varieties through the classification of the mid-infrared spectra of tea samples with noise, possibilistic fuzzy c-means clustering was applied in K-harmonic means clustering (KHM) and a novel clustering, called possibilistic fuzzy K-harmonic means clustering (PFKHM), was proposed. PFKHM can produce both fuzzy membership value and typicality value and solved the noise sensitivity problem of KHM. First of all, we used FTIR-7600 spectrometer to scan three varieties of tea samples (i. e. Emeishan Maofeng, high quality Leshan trimeresurus and low quality Leshan trimeresurus) for their Fourier transform infrared spectroscopy (FTIR) data. The wave number of FTIR data ranged from 4 001.569 to 401.121 1 cm-1. Secondly, we employed principal component analysis (PCA) to compress spectral data into 20-dimensional data which were compressed into two-dimensional data by linear discriminant analysis (LDA). Lastly, we used KHM and PFKHM to classify the tea varieties respectively. The experimental results indicated that when the weight index m=2, q=2 and p=2 the clustering accuracy rates of KHM and PFKHM achieved 91.67% and 94.44%, respectively. KHM was convergent after 12 iterations and PFKHM was convergent after 12 iterations. Tea varieties could be quickly and accurately classified by testing tea with FTIR technology, compressing spectral data with PCA and LDA, and classifying tea varieties with PFKHM.
Key words：Tea; Infrared spectroscopy; Principal component analysis; K-harmonic means clustering; Possibilistic fuzzy K-harmonic means clustering
[1] Cebi N, Yilmaz M T, Sagdic O. Food Chemistry, 2017, 229: 517.
[2] Anjos O, Santos A J A, Estevinho L M, et al. Food Chemistry, 2016, 205: 28.
[3] Ouyang Q, Liu Y, Chen Q, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2017, 180: 91.
[4] Li M, Dai G, Chang T, et al. Applied Sciences-Basel, 2017, 7: 172.
[5] Panigrahi N, Bhol C S, Das B S. Journal of Food Engineering, 2016, 190: 101.
[6] Zhuang X G, Wang L L, Wu X Y, et al. Journal of Infrared and Millimeter Waves, 2016, 35: 200.
[7] Shi W, Liu Y, Kong W, et al. Analytical Letters, 2015, 48(18): 2833.
[8] Jiang H, Chen Q. Food Analytical Methods, 2015, 8(4): 954.
[9] Wu X, Wu B, Sun J, et al. Applied Mathematical Modelling, 2015, 39(12): 3398.
[10] Yang F Q, Sun T L, Zhang C H. Expert Systems with Applications, 2009, 36: 9847.
[11] Jiang H, Yi S H, Li J, et al. Expert Systems with Applications, 2010, 37: 8679.
[12] Güngr Z, ünler A. Applied Mathematics and Computation, 2007, 184: 199.
[13] Güngr Z, ünler A. Applied Mathematical Modelling, 2008, 32: 1115.
[14] Pal N R, Pal K, Keller J M, et al. IEEE Transactions on Fuzzy Systems, 2005, 13: 517.
[15] Havens T, Bezdek J, Leckie C, et al. IEEE Transactions on Fuzzy Systems, 2012, 20: 1130.
