Multivariate sensitivity for the interpretation of the effect of spectral pretreatment methods on near-infrared calibration model predictions

Predictions obtained from a multivariate calibration model are sensitive to variations in the spectra such as baseline shifts, multiplicative effects, etc. Many spectral pretreatment methods have been developed to reduce thes e distortions, and the best method is usually the one that minimizes the pr ediction error for an independent test set. This paper shows how multivaria te sensitivity can be used to interpret spectral pretreatment results, Unde rstanding why a particular pretreatment method gives good or bad results is important for ruling out chance effects in the conventional process of "tr ial and error", thus obtaining more confidence in the finally selected mode l, The principles are exemplified using the transmission near-infrared spec troscopic prediction of oxygenates in ampules of the standard reference mat erial gasoline, The pretreatment methods compared are the multiplicative si gnal correction, first-derivative method, and second-derivative method. It is shown that for this application the first- and second-derivative methods are successful in removing the background. However, differentiating the sp ectra substantially reduces multivariate net analyte signal (in the worst c ase by a factor of 21), Consequently, a significantly smaller multivariate sensitivity is obtained which leads to increased spectral error propagation resulting in a larger uncertainty in the regression vector estimate and la rger prediction errors. Differentiating spectra also increases the spectral noise (each time by a factor 2(1/2)) but this effect, which is well-known, is of minor importance for the current application.