Publications Details
Outlier detection in multivariate calibration
Outlier samples can have very detrimental effects on the performances of multivariate calibration methods, as these methods are generally not very robust. Often, the software implementations of these methods do not check for outliers. If outliers are not detected, invalid predictions may result. This paper illustrates some simple exploratory procedures for detecting outliers with examples from near-infrared and mid-infrared spectroscopy using partial least-squares regression as the calibration method. 8 refs., 9 figs., 1 tab.