1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
2. High-tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, 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：Different foods contain different nutrients and effectiveness, and there are differences in their Fourier transform infrared spectra. In order to classify varieties of foods correctly, this paper presented the way to classify varieties by combining Fourier transform infrared spectroscopy (FTIR) with fuzzy clustering analysis. Fuzzy K-harmonic Kohonen clustering network (FKHKCN) was proposed by introducing fuzzy K-harmonic means (FKHM) clustering into the learning rate and update strategy of the Kohonen clustering network. The learning rate of FKHKCN is computed by fuzzy membership values of fuzzy C-means (FCM) clustering, and the cluster centers of FKHKCN can be derived from the cluster centers of FKHM. Therefore, FKHKCN can solve the problem that the Fuzzy Kohonen clustering network (FKCN) is sensitive to the initial cluster centers, and the clustering result is unstable. FKHKCN can achieve the clustering analysis of FTIR data as a fuzzy clustering algorithm. This experiment involves three datasets: (1) Three kinds of tea samples (Emeishan Maofeng, good and poor Leshan trimeresurus) were obtained from Sichuan, China as experimental samples with a total number of 96. (2) Two kinds of coffee samples (robusta and arabica). (3)Three meat samples (chicken, pork and turkey). To start with, three datasets were preprocessed. Scattering effects in the original spectra data of tea samples were reduced by multiple scattering correction (MSC). Savitzky-Golay was used to reduce noise in FTIR spectra of coffee and meat samples. Secondly, the high dimensional FTIR data of three datasets were reduced to by the low dimensionaldata by principal component analysis (PCA). Thirdly, tea data after PCA were extracted by linear discriminant analysis (LDA) and the spectral data were projected into the obtained discriminant vectors. Finally, FCM, FKCN and FKHKCN were used to classify the three datasets, respectively. The experimental results showed that FCM, FKCN and FKHKCN achieved the clustering accuracies for the tea varieties with the values: 90.91%, 90.91% and 93.94%, respectively; the clustering accuracies for the meat varieties with the values: 90.83%, 0.00% and 92.50%, respectively; the clustering accuracies for the coffee varieties with the values: 89.17%, 89.17% and 90.83%, respectively. The above experimental results indicated that FTIR technology coupled with PCA, LDA and FKHKCN was an effective method for classifying food varieties, and its clustering accuracy was higher than FCM and FKCN, and its clustering result was stable.
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