1. School of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
2. Institute of Scientific and Technical Information, Jiangsu University, Zhenjiang 212013, China
3. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Abstract：The freshness of lettuce is one of the most important factors affecting the lettuce quality, and it depends on the storage time. Therefore, it has important research value to identify the lettuce samples with different storage time accurately. Because the near-infrared reflectance (NIR) spectra of lettuce with different storage time have different characteristics, it is feasible to use NIR technology to identify lettuce with different storage time. Gath-Geva allied fuzzy c-means (GGAFCM) clustering was proposed to replacing the Euclidean distance in allied fuzzy c-means (AFCM) clustering with the exponential distance. By iterative computations, GGAFCM can produce fuzzy membership and typical values, which combine with near-infrared reflectance spectroscopy (NIRS) to achieve the classification of the lettuce samples with different storage time accurately. The experiment was conducted on fresh samples of lettuce, which were collected with Antaris Ⅱ spectrometer every 12 hours. The spectral wavenumber ranges from 10 000 to 4 000 cm-1. At first, by principal component analysis (PCA), the 1 557-dimensional spectra of lettuce samples were compressed to the 22-dimensional data whose discriminant information was extracted by fuzzy linear discriminant analysis (FLDA). As a result, the 22-dimensional data were transformed into the two-dimensional data by FLDA with two discriminant vectors. At last, the cluster centers of fuzzy c-means (FCM) clustering acted as the initial cluster centers of both GGAFCM and AFCM, and lettuce samples with different storage time were identified by FCM, GGAFCM and AFCM whose clustering accuracies, fuzzy membership values and iterative times were analyzed. Experimental results indicated that with the same initialization conditions, the GGAFCM algorithm adopted in this study has higher discrimination accuracy than FCM and AFCM. In the case of m=2, the discrimination accuracy of GGAFCM reached 95.56%, while the clustering accuracy of FCM and AFCM was 91.11%. GGAFCM converged after 4 iterations, while both AFCM and FCM needed 8 iterations to reach convergence. Based on NIRS, GGAFCM combined with PCA and FLDA can efficiently, quickly and nondestructively complete the accurate identification of lettuce samples with different storage time. It provides the experimental foundation and reference method for accurate and rapid identification of lettuce storage time and has certain practical application value.
Key words：Near-infrared reflectance spectroscopy; Lettuce; Storage time; Fuzzy linear discriminant analysis; The exponential distance; Fuzzy clustering
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