Chemometric Tools in the Analysis of Pharmaceutics Samples: a Comparison Among Several Multivariate Calibration Methods

Abstract

Bivariate calibration algorithm is compared with the results obtained by the usage of high-dimensional calibration methods such as partial least squares (PLS) and multi-way partial least-squares (N-PLS) by using UV-Vis spectrophotometric data of first and second-order. The algorithms were applied to the determination of a mixture of an analgesic and a stimulant compound and their actual concentrations of them were calculated by using spectroscopic data. The direct reading of absorbance values at 227 nm and 271 nm were employed for quantification of the compounds in the case of the bivariate method. The approaches of first-order and multi-way methods were applied with a previous optimization of the calibration matrix by constructing sets of calibration and validation with 20 and 10 samples (mixtures) respectively according to a central composite design and their UV absorption spectra were recorded at 200-350 nm. All algorithms were satisfactorily applied to the simultaneous determination of these compounds in pharmaceutical formulations with mean percentage recovery of 100.5 ± 3.67, 98.7 ± 3.42, and 100.5 ± 3.74 for bivariate, PLS-1, and N-PLS, respectively. The statistical evaluation of the bivariate method showed that this procedure is comparable with those algorithms that employ high-dimensional structured information. The aim of the work is to compare the methods under study and it can be seen that there are no significant differences, so a simple spectrophotometer can be used up to a very specialized one. However, the advantage of bivariate calibration is its simplicity, due to the minimal experimental manipulation.