Handling intrinsic non-linearity in near-infrared reflectance spectroscopy

The relationship between absorption in the near-infrared (NIR) spectral reg ion and the target analytical parameter is frequently of the non-linear typ e. The origin of the non-linearity can be widely varied and difficult to id entify. In some cases, the relationship between absorption and the analytic al parameter of interest is intrinsically non-linear owing to the very chem ical nature of the sample or analyte concerned. In this work, various multi variate calibration procedures were tested with a view to overcoming intrin sic 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 a re partial least squares (PLS) regression (with and without variable select ion), linear PLS with X projection (LP-PLS) and stepwise polynomial princip al component (SWP-PC) regression. Non-linear calibration methods included p olynomial PLS (PPLS) and artificial neural networks (ANNs). Results were co mpared on the basis of NIR spectra for ampicillin trihydrate samples, where the simultaneous presence of crystallization water and surface moisture gi ves rise to intrinsic non-linearity that affects the determination of the t otal water content in the sample. The best results were obtained by using t he non-linear calibration techniques. (C) 1999 Elsevier Science B.V. All ri ghts reserved.