Improving the Robustness and Stability of Partial Least Squares Regression for Near-infrared Spectral Analysis

✍ Scribed by Xueguang SHAO; Da CHEN; Heng XU; Zhichao LIU; Wensheng CAI

• Publisher: John Wiley and Sons
• Year: 2009
• Tongue: English
• Weight: 94 KB
• Volume: 27
• Category: Article
• ISSN: 0256-7660
• DOI: 10.1002/cjoc.200990222

✦ Synopsis

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.