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偏最小二乘回归用于近红外光谱分析的稳健策略
引用本文:邵学广,陈达,徐恒,刘智超,蔡文生.偏最小二乘回归用于近红外光谱分析的稳健策略[J].中国化学,2009,27(7):1328-1332.
作者姓名:邵学广  陈达  徐恒  刘智超  蔡文生
作者单位:南开大学化学学院, 天津, 300071
摘    要:偏最小二乘法(PLS)在近红外光谱(NIR)定量分析中占有重要地位,但预测结果往往容易受到样本分组和奇异样本等因素的影响,稳健性不强。多模型PLS (EPLS)方法在模型稳健性上得到提高,然而它无法识别样本中存在的奇异样本。为了同时提高模型的预测准确性和稳健性,本文提出了一种根据取样概率重新取样的多模型PLS方法,称为稳健共识PLS(RE-PLS)方法。该方法通过迭代赋权偏最小二乘法(IRPLS)计算样本回归残差得到每个校正集样本的取样概率,然后根据样本的取样概率来选择训练子集建立多个PLS模型,最后将所有PLS模型的预测结果平均作为最终预测结果。该方法用于两种不同植物样品的近红外光谱建模,并与传统的PLS及EPLS方法进行比较。结果表明该方法可以有效的避免校正集中奇异样本对模型的影响,同时可以提高预测精确度和稳健性。对于含有较多奇异样本的,复杂近红外光谱烟草实际样本,利用简单PLS或者EPLS方法建模预测效果不是很理想,而RE-PLS凭借其独特优势则有望在这种复杂光谱定量分析中得到广泛的应用。

关 键 词:near-infrared  spectroscopy    probability  resampling    robust  modeling    ensemble  partial  least  squares
收稿时间:2008-6-16
修稿时间:2009-1-20

Improving the Robustness and Stability of Partial Least Squares Regression for Near‐infrared Spectral Analysis
Xueguang SHAO,Da CHEN,Heng XU,Zhichao LIU,Wensheng CAI.Improving the Robustness and Stability of Partial Least Squares Regression for Near‐infrared Spectral Analysis[J].Chinese Journal of Chemistry,2009,27(7):1328-1332.
Authors:Xueguang SHAO  Da CHEN  Heng XU  Zhichao LIU  Wensheng CAI
Institution:1. Tel.: 0086‐022‐23503430;2. Fax: 0086‐022‐23502458;3. Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin 300071, China
Abstract:Partial least‐squares (PLS) regression has been presented as a powerful tool for spectral quantitative measurement. However, the improvement of the robustness and stability of PLS models is still needed, because it is difficult to build a stable model when complex samples are analyzed or outliers are contained in the calibration data set. To achieve the purpose, a robust ensemble PLS technique based on probability resampling was proposed, which is named RE‐PLS. In the proposed method, a probability is firstly obtained for each calibration sample from its residual in a robust regression. Then, multiple PLS models are constructed based on probability resampling. At last, the multiple PLS models are used to predict unknown samples by taking the average of the predictions from the multiple models as final prediction result. To validate the effectiveness and universality of the proposed method, it was applied to two different sets of NIR spectra. The results show that RE‐PLS can not only effectively avoid the interference of outliers but also enhance the precision of prediction and the stability of PLS regression. Thus, it may provide a useful tool for multivariate calibration with multiple outliers.
Keywords:near‐infrared spectroscopy  probability resampling  robust modeling  ensemble partial least squares
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