Optimization of Experimental Parameters in Analysis of Pharmaceutical Pellets by Near Infrared-Chemical Imaging (NIR-CI) and Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS)

Abstract

The quality control of pellets homogeneity cannot be assessed by conventional techniques and near infrared-chemical imaging combined with multivariate curve resolution with alternating least squares is an attractive alternative. In this study, composition and spatial distribution of pellets components were determined after assessment of experimental parameters. The use of a 25 μm intermediate pixel size, an initial estimation matrix with instrumental signals for pure substances and individual matrices provided a model with explained variance of more than 99% and a value of 0.00263 for percentage of lack of fit. In addition, the similarity between the pure substances spectra and those recovered by the model were 0.9501 for sucrose, 0.9480 for starch, 0.9910 for ketoprofen and 0.5941 for SiO 2 . Chemical images were generated and show that the pellet is composed of an inert nucleus of starch and cellulose, surrounded by a ketoprofen layer. All this information was obtained quickly, in minutes, being an excellent alternative for pellets analysis.