Artificial neural networks versus partial least squares and multivariate resolution-alternating least squares approaches for the assay of ascorbic acid,rutin, and hesperidin in an antioxidant formulation

Abstract

This study was concerned with the assay of ascorbic acid (ASC), rutin, and hesperidin (HES) in their combined formulation using a multivariate approach. Three chemometric-assisted spectrophotometric models namely: partial least squares, multivariate curve resolution-alternating least squares, and artificial neural networks were developed and validated. The quantitative analyses of all the proposed models were assessed by percentage recoveries, root mean square error of prediction, and standard error of prediction. The proposed models were used in the range of 10.0–70.0, 2.0–10.0, and 2.0–10.0?µg mL^?1 for ASC, rutin, and HES, respectively. In addition, correlation coefficients between the pure and estimated spectral profiles were used for the qualitative analysis of a multivariate curve resolution-alternating least squares model. Artificial neural networks showed higher speed and methodological simplicity over the other two models. These models presented powerful multivariate statistical tools that were applied to the analysis of the combined dosage form in the Australian market. They have the ability to overcome difficulties such as colinearity and spectral overlaps. Statistical comparison between the proposed and reported methods showed no significant difference. The proposed methods can be used for the routine analysis of the studied drugs in quality control laboratories.