GA-PLS结合PC-ANN算法提高奶粉蛋白质模型精度

doi:10.3964/j.issn.1000-0593(2009)07-1818-04

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Abstract The present paper presents a new NIR analysis method with partial least square regression (PLS) and artificial neural network (ANN) to improve the prediction precision of the protein model for milk powder. First, an efficient method named region selecting by genetic algorithms (RS-GA) was used to select the calibration region, and then the GA-PLS model was made to predict the linear part of the protein content in milk powder. And then in the region selected by RS-GA method, principal component analysis (PCA) was calculated. The principal components were taken as the input of ANN model. The remnant values by subtracting the standard values and the GA-PLS validation values were regarded as the output of ANN. The ANN model was made to predict the nonlinear part of the protein content. The final result of the model was the addition of the two model’s validation values, and the root mean squared error of prediction (RMSEP) was used to estimate the mixed model. A full region PLS model (Fr-PLS) was also made, and the RMSEP of the Fr-PLS, GA-PLS and GA-PLS+PC-ANN model was 0.511, 0.440 and 0.235, respectively. The results show that the prediction precision of the protein model for milk powder was largely improved when adding the nonlinear port in the NIR model, and this method can also be used for other complex material to improve the prediction precision.

Key words： NIRS GA-PLS PC-ANN Model accuracy Milk powder Protein

Received: 2008-03-26 Accepted: 2008-06-28

ZTFLH:

O657.3

Corresponding Authors: HAN Dong-hai E-mail: handh@cau.edu.cn

Cite this article:

SUN Qian,WANG Jia-hua,HAN Dong-hai. Improving the Prediction Model of Protein in Milk Powder Using GA-PLS Combined with PC-ANN Arithmetic [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2009, 29(07): 1818-1821.

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http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2009)07-1818-04 OR http://www.gpxygpfx.com/EN/Y2009/V29/I07/1818

[1] WU Jing-zhu, WANG Yi-ming, ZHANG Xiao-chao, et al(吴静珠, 王一鸣, 张小超, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2007, 27(9): 1735.
[2] WEI Shao-hua, YANG Li, WU Jian-guo(魏少华, 杨莉, 吴建国). Journal of Diary Science and Technology(乳品科学与技术), 2003, (2): 57.
[3] ZHENG Li-min，ZHANG Lu-da，GUO Hui-yuan，et al(郑丽敏，张录达，郭慧媛，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2007, 27 (11): 2224.
[4] PI Fu-wei，WANG Yan-ling，LU Chao，et al(皮付伟，王燕玲，鲁超，等). Modern Scientific Instruments(现代科学仪器)，2006, (4): 34.
[5] LI Yan-xiao，ZOU Xiao-bo，DONG Ying(李艳肖，邹小波，董英). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(10): 2001.
[6] Zhang W，Xiang Bingren. Analytica Chimica Acta, 2007, 585(1): 55.
[7] GU Xiao-yu，XU Ke-xin，WANG Yan(谷筱玉，徐可欣，汪曣). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(9): 1618.
[8] Ying Yibin，Liu Yande. Journal of Food Engineering, 2008, 84: 206.
[9] LIU Hui-jun，Lü Jin，LIN Min，et al(刘辉军，吕进，林敏，等). Journal of Instrumental Analysis(分析测试学报), 2006, 26(5): 679.
[10] Durand A，Devos O，Ruckebusch C，et al. Analytica Chimica Acta, 2007, 595: 72.
[11] LI Xiao-li，HE Yong，QIU Zheng-jun(李晓丽，何勇，裘正军). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(2): 279.
[12] Shao Yongni, He Yong, Feng Shuijuan. Food Research International, 2007, 40: 835.
[13] Roman M B, Ravilya Z S, Ekaterina I L. Chemometrics and Intelligent Laboratory Systems, 2007, 88: 183.
[14] Li Xiaoli, He Yong, Fang Hui. Journal of Food Engineering, 2007, 81: 357.
[15] Lü Hui-ying, REN Yu-lin, LIU Ming-yang(吕慧英，任玉林，刘名扬). Chemical Journal of Chinese Universities(高等学校化学学报), 2007, 28(5): 847.
[16] SHAO Yong-ni, HE Yong(邵咏妮，何勇). Journal of Infrared Millimeter Waves(红外与毫米波学报), 2006, 25(6): 478.
[17] CHEN Jian，CHEN Xiao, LI Wei, et al(陈建, 陈晓, 李伟, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2008, 28(8): 1806.
[18] CHU Xiao-li, YUAN Hong-fu, LU Wan-zhen(褚小立，袁洪福，陆婉珍). Progress in Chemistry(化学进展), 2004, 16(4): 528.