Multicomponent Quantitative Analysis Using Near Infrared Spectroscopy by Building PLS-GRNN Model
LIU Bo-ping1,2,QIN Hua-jun3,LUO Xiang2,4,CAO Shu-wen3,WANG Jun-de1*
1. College of Chemical Engineering, Nanjing University of Science and Technology, Nanjing 210014，China 2. Analytical and Testing Center of Jiangxi Province, Nanchang 330029, China 3. Key Laboratory of Food Science of MOE, Nanchang University, Nanchang 330047, China 4. Department of Chemistry, Nanchang University, Nanchang 330047, China
Abstract：The present paper introduces an application of near infrared spectroscopy(NIRS) multi-component quantitative analysis by building partial least squares (PLS)-generalized regression neural networks (GRNN) model. The PLS-GRNN prediction model for chlorine, fibre and fat in 45 feedstuff samples was established with good veracity and recurrence. Eight peak values in principal components compressed from original data by PLS and four in original spectra were taken as inputs of GRNN while 4 predictive targets as outputs. 0.1 was chosen as smoothing factor for its good approximation and prediction with the lowest error compared with 0.2, 0.3, 0.4 and 0.5. Predictive correlation coefficient and Standard error of the estimate of three components by the model are 0.984 0, 0.987 0 and 0.983 0, and 0.015 89, 0.154 1 and 0.115 1，while the Standard deviations of an unknown sample scanned 8 times are 0.003 26, 0.065 5 and 0.031 4. The results show that PLS-GRNN used in NIRS is a rapid, effective means for measuring chlorine, fibre in the fat in feedstuff powder, and can also be used in quantitative analysis of other samples. A settlement in the high error of prediction of other samples with lower contents was also shown.
[1] XU Guang-tong, YUAN Hong-fu, LU Wan-zhen(徐广通, 袁洪福, 陆婉珍). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2000, 20(2): 134. [2] McClure W F, Crowell B, Stanfield D L, et al. Journal of Near Infrared Spectrosc., 2002, 10: 177. [3] CHENG Zhong, CHEN De-zhao(成 忠, 陈德钊). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2006, 26(6): 1046. [4] HE Yong, LI Xiao-li, SHAO Yong-ni(何 勇, 李晓丽, 邵咏妮). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2006, 26(5): 853. [5] Yande Liu, Yibin. Postharvest Biology and Technology, 2005, 37: 65. [6] Marin Navrtil, Anna Norberg, Lars Lembrn, et al. Journal of Biotechnology, 2005, 115(1): 67. [7] ZHANG Lin, ZHANG Li-ming, LI Yan, et al(张 琳, 张黎明, 李 燕, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2005, 20(6): 775. [8] LI Yan, SUN Xiu-yun, WANG Jun-de(李 燕, 孙秀云, 王俊德). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2000, 25(10): 1610. [9] Melkon Tatlier, Kerem H Cigizoglu, Ayse, Erdem-Senatalar. Computers and Chemical Engineering, 2005, 30: 137. [10] Gavin J Bowden, Holger R Maier, Graeme C Dandy. Journal of Hydrology, 2005, 301: 93. [11] Cigizoglu H K, Alp M. Advances in Engineering Software, 2006, 37(2): 63. [12] Savita G Kulkarni, Amit Kumar Chaudhary, Somnath Nandi, et al. Biochemical Engineering Journal, 2004, 18: 193. [13] YANG Jian-gang(杨建刚). Applied Tutorial of Artificial Neural Networks(人工神经网络实用教程). Hangzhou: Zhejiang University Press(杭州: 浙江大学出版社), 2001. [14] W M Lee, Lee Y Y, Lim C P, et al. Advanced Engineering Informatics. 2006, 20(2): 213. [15] LIN Hong-bo, LI Yue, MA Hai-tao, et al(林红波，李 月，马海涛，等). Progress in Geophysics(地球物理学进展)，2003，18(4)：743. [16] WANG Cun-you, YU Jia-yuan(汪存友，余嘉元). Computer Applications(计算机应用)，2006，26(4)：992.
