Abstract：Electronic absorption spectroscopy (EAS), as an indirect analytical technique, has been used to carry out quantitative analysis of unknown samples by establishing a model with calibration samples. Partial least squares (PLS), as a powerful technique for process modeling and multivariate statistical process control, has been widely used to establish this model. On account of the noise signal in the spectra, the signal preprocessing was often a necessary step in building reliable and robust multivariate calibrations. The main goal of preprocessing was to remove variation in the data that was irrelevant to modeling. Orthogonal signal correction (OSC) and related methods emerged as filtering techniques for various spectra in modern chemometrics literature. However, it was confirmed in recent work that preprocessing with OSC did not lead to any significant improvements in calibration models subsequently developed by means of PLS regression, except for merely reducing the number of latent variables in the PLS model by the number of OSC components removed. Now in our study, taking into account the local effect sensitivity and numerous predictor variables with serious multicollinearity of the spectra data, a novel PLS algorithm that embedded the OSC into the regression framework of the PLS, termed as POSC-PLS method, was implemented. It firstly applied the OSC technique to a set of selected spectra at an optimized size of moving window, namely piecewise OSC (POSC), to pretreat the spectra matrix and eliminate the local variance, thus the spectra matrix pretreated was taken as the new independent variables matrix, then the PLS algorithm was applied to build the calibration model. Finally, application of the proposed POSC-PLS approach to the EAS quantitative analysis of the polyaromatic hydrocarbons(PAHs)was presented for comparison with the MLR (multiple linear regression), PLS and OSC-PLS methods. The result indicates that the POSC-PLS approach by performing POSC prior to calibration not only can improve the model accuracy, but also decreases the PLS factors compared to the models obtained by the above rest methods and so its resulting model becomes more concise. The removal of orthogonal components from the response matrix is greatly facilitated simply by considering localized spectral features. So, preprocessing with POSC was shown to benefit the multivariate PLS model because it performed a localized regression modeling procedure that differs from that of PLS. At the same time, the POSC is a potential chemometric technique in the pretreatment of various spectra.
Key words：Orthogonal signal correction;Partial least square;Electronic absorption spectroscopy;Quantitative analysis;Polyaromatic hydrocarbons (PAHs
[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), 2001, 21(4)：459. [2] HUANG Jian-rong, LI Tong-hua, CHEN Kai, et al(黄建荣, 李通化, 陈 开，等). Chemical Journal of Chinese Universities(高等学校化学学报), 2003, 24(6)：1009. [3] 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), 2006, 26(9)：1614. [4] Geladi P. Journal of Chemometrics, 1988, 2(4)：231. [5] LI Jun-hui，QIN Xi-yun，ZHANG Wen-juan，et al(李军会, 秦西云, 张文娟, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2007, 27(2)：262. [6] Wold S, Antti H, Lindgren F, et al. Chemometrics and Intelligent Laboratory Systems, 1998，44：175. [7] Sjoblom J, Svensson O, Wold S, et al. Chemometrics and Intelligent Laboratory Systems, 1998, 44：229. [8] Andersson C A. Chemometrics and Intelligent Laboratory Systems, 1999, 47：51. [9] Fean T. Chemometrics and Intelligent Laboratory Systems, 2000, 50：47. [10] Westerhuis J A, De Jong S, Smilde A K. Chemometrics and Intelligent Laboratory Systems, 2001, 56：13. [11] Trygg J, Wold S. Journal of Chemometrics, 2002, 16：119. [12] Geladi P, Kowalski B R. Analytica Chimica Acta, 1986, 185(1)：1. [13] Feudale R N, Tan H, Brown S D. Chemometrics and Intelligent Laboratory Systems, 2002, 63：129. [14] WANG Hui-wen(王惠文). Partial Least-Squares Regression Method and Applications (偏最小二乘回归方法及其应用). Beijing: National Defence Industry Press(北京：国防工业出版社), 1999. [15] Brereton R G. Chemometrics: Data Analysis for the Laboratory and Chemical Plant. Wiley, Chichester. 2003. [16] Xu Q S, De J S, Lewi P, et al. Chemometrics and Intelligent Laboratory Systems, 2004, 71(1)：21.
