Handling intrinsic non-linearity in near-infrared reflectance spectroscopy

The relationship between absorption in the near-infrared (NIR) spectral region and the target analytical parameter is frequently of the non-linear type. The origin of the non-linearity can be widely varied and difficult to identify. In some cases, the relationship between absorption and the analytical parameter of interest is intrinsically non-linear owing to the very chemical nature of the sample or analyte concerned. In this work, various multivariate calibration procedures were tested with a view to overcoming intrinsic non-linearity in NIR reflectance. An approach to solving the problem is suggested. Calibration was done, after transformation of spectra, by using linear and non-linear techniques. The linear calibration techniques used are partial least squares (PLS) regression (with and without variable selection), linear PLS with X projection (LP-PLS) and stepwise polynomial principal component (SWP-PC) regression. Non-linear calibration methods included polynomial PLS (PPLS) and artificial neural networks (ANNs). Results were compared on the basis of NIR spectra for ampicillin trihydrate samples, where the simultaneous presence of crystallization water and surface moisture gives rise to intrinsic non-linearity that affects the determination of the total water content in the sample. The best results were obtained by using the non-linear calibration techniques.