Outlier detection in near-infrared spectroscopic analysis by using Monte Carlo cross-validation

• 来源期刊: 中国科学B辑（英文版）
• 年，卷（期）: 2008,（8）
• 页码范围: 751-759
• 页数: 9页
• 语种: 英文
• DOI: 10.1007/s 11426-008-0080-x

摘要

An outlier detection method is proposed for near-infrared spectral analysis. The underlying philosophy of the method is that, in random test (Monte Carlo) cross-validation, the probability of outliers pre-senting in good models with smaller prediction residual error sum of squares (PRESS) or in bad mod-els with larger PRESS should be obviously different from normal samples. The method builds a large number of PLS models by using random test cross-validation at first, then the models are sorted by the PRESS, and at last the outliers are recognized according to the accumulative probability of each sam-ple in the sorted models. For validation of the proposed method, four data sets, including three pub-lished data sets and a large data set of tobacco lamina, were investigated. The proposed method was proved to be highly efficient and veracious compared with the conventional leave-one-out (LOO) cross validation method.