Characterization of the sources of variation affecting near-infrared spectroscopy using chemometric methods

A rapid assessment of product quality can often be made using a combination of near-infrared spectroscopy (NIR) and multivariate calibration. The robustness of such a method is determined by the sensitivity of the multivariate calibration model to variations in the spectral data. An approach is described that uses a combination of experimental design methodology and principal component analysis to identify the main sources of variation in the spectra and to estimate their influence on the quantitative predictions. This is accomplished by comparing variations in a set of measured, replicate spectra to spectra with simulated variations. The approach was applied to the hydroxyl number determination of polyols by NIR spectroscopy and partial least-squares calibration. The results indicated that the most significant sources of variation were due to a variable cell path length and a variable curved background. Correction for these errors resulted in a 58% reduction in the standard deviation of the hydroxyl number predictions, indicating that a substantial improvement in the method precision is possible.