Possibilistic Fuzzy K-Harmonic Means Clustering of Fourier Transform Infrared Spectra of Tea

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 Fl IR data ranged from 4 001. 569 to 401. 121 1 cm'. 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.