Study on Characteristic Bands Selection of Lamb pH Value Based on Hyperspectral Imaging and Partial Least Squares(PLS)
ZHU Rong-guang1, DUAN Hong-wei1, YAO Xue-dong1, QIU Yuan-yuan1, MA Ben-xue1, XU Cheng-jian2
1. College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China 2. Food College, Shihezi University, Shihezi 832003, China
Abstract：Characteristic bands method selection and subsequent spectral extraction has a great influence on the hyperspectral model performance. For rapid and accurate detection of mutton pH value, the effects of 2 band-selection methods on PLS models of mutton pH based on HSI technique were carried out and discussed. Initially, the preprocessing method of second derivative (2D), multiplicative scatter correction (MSC) and mean-centering together was implemented on the representative spectra of mutton muscle portion. Then, 2 methods of synergy interval partial least square (siPLS) and the combination of synergy interval partial least squares with genetic algorithm (siPLS-GA) were used to extract the characteristic bands in the spectral range of 473～1 000 nm. Finally, 2 PLS models of lamb pH value were established with the corresponding characteristic bands, and were also compared with the effect of full-band PLS model. The results indicated that the effect of siPLS-GA-PLS model was the best. As for the siPLS-GA-PLS model, 56 characteristic wavelength points were chosen, the correlation coefficient(Rcal) and root mean square error(RMSEC) of calibration set was 0.96 and 0.043 respectively, and the correlation coefficient(Rp) and root mean square error(RMSEP) of prediction set was 0.96 and 0.048 respectively. Spectral variables were reduced and model accuracy was improved. It can be concluded that characteristic bands selection and rapid and accurate detection of lamb pH can be achieved using hyperspectral imaging technique combined with siPLS-GA method.
Key words：Hyperspectral imaging(HSI);Lamb pH;Characteristic bands;Synergy interval partial least square(siPLS);Genetic algorithm(GA)
朱荣光1，段宏伟1，姚雪东1，邱园园1，马本学1，许程剑2 . 基于高光谱图像和偏最小二乘的羊肉pH值特征波段筛选研究 [J]. 光谱学与光谱分析, 2016, 36(09): 2925-2929.
ZHU Rong-guang1, DUAN Hong-wei1, YAO Xue-dong1, QIU Yuan-yuan1, MA Ben-xue1, XU Cheng-jian2 . Study on Characteristic Bands Selection of Lamb pH Value Based on Hyperspectral Imaging and Partial Least Squares(PLS). SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(09): 2925-2929.
[1] Chen Q, Zhang Y, Zhao J, et al. Analytical Methods, 2013, 5(22): 6382. [2] ElMasry G, Sun D W, Allen P. Journal of Food Engineering, 2012, 110(1): 127. [3] Wu D, Wang S, Wang N, et al. Food and Bioprocess Technology, 2013, 6(11): 2943. [4] Peng Y, Zhang J, Wang W, et al. Journal of Food Engineering, 2011, 102(2): 163. [5] ZHU Rong-guang, YAO Xue-dong, GAO Guang-di, et al(朱荣光，姚雪东，高广娣, 等). Journal of Agricultural Machinery(农业机械学报), 2013, 44(7): 165. [6] ZHU Wei-xing, JIANG Hui, CHEN Quan-sheng(朱伟兴，江 辉，陈全胜). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2010, 26(8): 368. [7] WU Rui-mei, YUE Peng-xiang, ZHAO Jie-wen, et al(吴瑞梅，岳鹏翔，赵杰文, 等). Journal of Agricultural Machinery(农业机械学报), 2011, 42(12): 154. [8] ZHU Yao-di, ZOU Xiao-bo, SHI Ji-yong, et al(朱瑶迪，邹小波，石吉勇，等). Modern Food Science and Technology(现代食品科技), 2014, 30(12): 119. [9] ZOU Xiao-bo, HUANG Xiao-wei, SHI Ji-yong, et al(邹小波，黄晓玮，石吉勇, 等). Journal of Agricultural Machinery(农业机械学报), 2012, 43(9): 155. [10] Wang W, Li C, Tollner E W, et al. Journal of Food Engineering, 2012, 109(1): 38. [11] Kamruzzaman M, ElMasry G, Sun D W, et al. Innovative Food Science & Emerging Technologies, 2012, 16: 218. [12] LI Min-zan, HAN Dong-hai, WANG Xiu(李民赞，韩东海，王 秀). Spectral Analysis Technology and Application(光谱分析技术及其应用). Beijing: Science Press(北京：科学出版社), 2006
