Multiple outlier detection for multivariate calibration using robust statistical techniques

Outliers that are incorporated into a multivariate calibration model can significantly reduce the performance of the model. In the case of multiple outliers, the standard methods for outlier detection can fail to detect true outliers and even mistakenly identify good samples as outliers. Robust statistical methods are less sensitive to outliers and can provide a powerful tool for the reliable detection of multiple outliers. This paper examines the use of robust principal component regression (PCR) and iteratively reweighted partial least squares (PLS) for multiple outlier detection in an infrared spectroscopic application.