Multivariate calibration techniques applied to derivative spectroscopy data for the analysis of pharmaceutical mixtures

The prediction power of different chemometric methods was compared when applied on ordinary UVspectra and first-to-fourth order derivative spectra. Principal component regression (PCR) and partial leastsquares with one dependent variable (PLS-1) and three dependent variables (PLS2) were applied on spectraldata of pharmaceutical formulations containing paracetamol, propiphenazone and caffeine. Derivatizationability in resolving spectral overlapping was evaluated when the multivariate methods are adopted foranalysis of multicomponent mixtures. The chemometric models were tested on an external validationdataset and finally applied to the analysis of commercial formulations containing two or three drugs. Themodels were optimized by selecting the wavelength regions to be used in calibration through a new methodwhich ensured either an acquisition of useful information or a removal of redundant or noisy data. Thisprocedure provided to evaluate the analytical information of the wavelengths by using the componentregression coefficients calculated in multivariate regressions. Significant advantages were found in analysis ofall the analytes when the calibration models from third-order derivative spectra were used, showing relativestandard errors less than 1.4%. In contrast, the other derivative orders displayed higher variance and their usegave inaccurate results.