No abstract

The influence of particle size on near-infra red (NIR) spectra is typically considered a 'nuisance factor' which many scatter correction methods attempt to eliminate, e.g., multiplicative scatter correction. However, particle size is a key issue in the formulation of many pharmaceutical products and has a profound effect on the behaviour of both raw materials and drug substances during formulation. NIR has already been demonstrated as a potential alternative particle sizing technique to current accepted methodology. This investigation assessed several chemometric approaches that model this information, using lactose monohydrate as the raw material. A variety of modelling techniques were applied to both zero order and second derivative spectra namely multiple linear regression, partial least squares, principal component regression and artificial neural networks. One further data transformation evaluated was polar coordinates, although no statistical data were generated. Typically, cross-validation root mean square errors of calibration and cross-validation root mean square errors of prediction of approximately 5 microns were calculated for all of the modelling techniques. These values are comparable to those associated with the reference technique (laser diffractometry). Correlation coefficients of approximately 0.98 for all techniques were also calculated. The predictive abilities for models generated using second derivative spectra were found to be comparable to those obtained using zero order spectra.