| 光谱学与光谱分析 |
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| Discrimination of Lettuce Storage Time Using Near Infrared Spectroscopy Based on Generalized Fuzzy K-Harmonic Means Clustering |
| WU Xiao-hong1, 2, PAN Ming-hui3, WU Bin4, JI Gang1, SUN Jun1, 2 |
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. Jingjiang College, Jiangsu University, Zhenjiang 212013, China
4. Department of Information Engineering, Chuzhou Vocational Technology College, Chuzhou 239000, China |
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Abstract Lettuce storage time is an important factor affecting the lettuce freshness. To realize the non-destructive, rapid and effective discrimination of lettuce storage time, generalized fuzzy K-harmonic means (GFKHM) clustering was proposed by introducing the pth power of Euclidean distance into fuzzy K-harmonic means (FKHM) clustering to replace the square of Euclidean distance in FKHM, and furthermore GFKHM was applied in the discrimination of lettuce storage time. Sixty fresh lettuce samples were prepared as the research object, and the near infrared reflectance (NIR) spectra of lettuces were collected by Antaris Ⅱ near infrared spectrometer with a spectral range of 10 000～4 000 cm-1 for three 12-hour detections. Firstly, the 1 557-dimensional NIR spectra were reduced by principal component analysis (PCA) to decrease redundant information. After the first 20 principal components were selected, PCA translated the 1 557-dimensional NIR spectra into the 20-dimensional data. Secondly, linear discriminant analysis (LDA) was used to extract the discriminant information from the 20-dimensional data to improve the clustering accuracy. With the first two discriminant vectors, LDA translated the 20-dimensional data into the two-dimensional data. Finally, the cluster centers from fuzzy C-means clustering (FCM) were set as the initial cluster centers for FKHM and GFKHM and fuzzy membership values of FKHM and GFKHM were calculated to identify lettuce storage time. The experimental results showed that the discrimination accuracy of GFKHM has achieved 92.5% which was higher than that of FKHM. The cluster centers of GFKHM were much closer to the true cluster centers in comparison with FKHM. Furthermore, the convergence of the GFKHM was significantly faster than FKHM. Near infrared spectroscopy coupled with GFKHM, PCA and LDA could cluster NIR spectra of lettuce quickly and correctly, and this provided a fast and nondestructive method for identifying lettuce storage time.
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Received: 2015-01-28
Accepted: 2015-04-22
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Corresponding Authors:
WU Xiao-hong
E-mail: wxh_www@163.com
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[1] SUN Jun, JIN Xia-ming, MAO Han-ping, et al(孙 俊，金夏明，毛罕平，等). Chinese Journal of Analytical Chemistry(化学分析), 2014, 42(5): 672.
[2] Peng X, Yang J, Cui P, et al. LWT-Food Science and Technology, 2015, 60(1): 300.
[3] Simon A H, Stewart F G, Emmanuelle C, et al. Food Chemistry, 2013, 136(3-4): 1557.
[4] Warawut S, Guangli N, Liu R, et al. Computers and Electronics in Agriculture, 2013, 91: 87.
[5] Panmanas S, Munehiro T, Takayuki K, et al. Journal of Food Engineering, 2012, 112(3): 218.
[6] He W, Zhou J, Cheng H, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2012, 86: 399.
[7] Sun J, Jin X M, Mao H P, et al. Journal of Pure and Applied Microbiology, 2013, 7: 747.
[8] ZHAO Heng, YANG Wan-hai, ZHANG Gao-yu(赵 恒，杨万海，张高煜). Journal of Xidian University(西安电子科技大学学报), 2005, 32(4)： 603.
[9] Abdeyazdan M. The Journal of Supercomputing, 2014, 68(2): 574.
[10] Hung C H, Chiou H M, Yang W N. Applied Mathematical Modelling, 2013, 37(24): 10123.
[11] Bobrowski L, Bezedek J C. IEEE Transaction on SMC, 1991, 21(3): 545.
[12] Gou J, Hou F, Chen W, et al. Neurocomputing, 2015, 151(3): 1293.
[13] Pal N R, Pal K, Bezdek J C. IEEE Trans. Fuzzy Systems, 2005, 13(4): 517.
[14] Dsclescu S, Iovanov M C, Predu瘙塅 S. Linear Algebra and Its Applications, 2013, 439(10): 3166.
[15] WU Xiao-hong, ZHOU Jian-jiang(武小红，周建江). Acta Electronica Sinica(电子学报), 2008, 36(10): 1996. |
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