摘要: 采用全谱建立多元校正模型时,通常计算量大,模型不够稳健,而且模型的预测精度往往也不能达到最优。文章介绍一种新的波长选择方法:采用连续投影算法(successive projections algorithm),并将其集成偏最小二乘(partial least squares)多变量校正技术构成SPA-PLS方法,用于谷物小麦近红外光谱波长优化选择及其与水分含量的定量分析。结果表明:在经SPA算法后,光谱波数可削减97.72%,后继的定量校正模型结构得到显著简化,模型的稳健性也大大增强;同时,被选取的波长物理意义明确,模型的解释能力增强,而模型的预测性能也与GA-PLS方法相当。
关键词:连续投影算法;偏最小二乘;波长选择;近红外光谱;定量分析;小麦
Abstract:Successive projections algorithm combined with partial least squares regression, termed as SPA-PLS approach, was implemented as a novel variable selection approach to multivariate calibration. The proposed approach was applied to near-infrared reflectance data for analyzing moisture in wheat. The number of variables selected from 701 spectral variables was reduced to 16 by SPA, and the root mean squared error of prediction set (RMSEP) of the corresponding partial least squares regression models was decreased to 0.205 5% as well. The result indicates that the SPA-PLS approach by performing SPA prior to calibration not only can improve the model accuracy, but also decreases the number of spectral variables, so its resulting model becomes more concise. Moreover, as compared with genetic algorithm for wavelength selection, SPA is a deterministic search technique whose results are reproducible and it is more robust with respect to the choice of the validation set.
Key words:Successive projections algorithm;Partial least square;Wavelength selection;Near-infrared spectroscopy;Quantitative analysis;Wheat
[1] SUN Tong, XU Hui-rong, YING Yi-bin(孙 通,徐惠荣,应义斌). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2009,29(1):122. [2] ZHU Xiang-rong, LI Na, SHI Xin-yuan, et al(朱向荣,李 娜,史新元,等) . Chemical Journal of Chinese Universities(高等学校化学学报),2008,29(5):906. [3] HAO Yong, CAI Wen-sheng, SHAO Xue-guang(郝 勇,蔡文生,邵学广). Chemical Journal of Chinese Universities(高等学校化学学报),2009,30(1):28. [4] 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. [5] CHENG Biao, CHEN De-zhao, WU Xiao-hua(成 飙,陈德钊,吴晓华). Chinese Journal of Analytical Chemistry(分析化学),2006,34(特刊):S123. [6] Han Qingjuan, Wu Hailong, Cai Chenbo, et al. Analytica Chimica Acta,2008,612(2):121. [7] Thomas E V. Anal. Chem.,1994,66:795A. [8] Spiegelman C H, McShane M J, Cote G L, et al. Anal. Chem.,1998,70:35. [9] Bregman L. Dokl. Akad. Nauk SSSR,1965,162(3):487. [10] Araújo M C U, Saldanha T C B, Galvo R K H, et al. Chemometrics and Intelligent Laboratory Systems,2001,57:65. [11] Nezio M S D, Pistonesi M F, Fragoso W D. Microchemical Journal,2007,85:194. [12] Galvo R K H, Araújo M C U, Fragoso W D, et al. Chemometrics and Intelligent Laboratory Systems,2008,92:83. [13] Geladi P, Kowalski B R. Analytica Chimica Acta,1986,185(1):1. [14] Kalivas J H. Chemometrics and Intelligent Laboratory Systems,1997,37:255.