Simultaneous determination of two active components in compound aspirin tablets using principal component artificial neural networks (PC-ANNs) on NIR spectroscopy

Abstract

A method for simultaneous, non-destructive analysis of aspirin and phenacetin in compound aspirin tablets with different concentrations has been developed by principal component artificial neural networks (PC-ANNs) on near-infrared (NIR) spectroscopy. In PC-ANNs models, the spectra data were first analyzed by principal component analysis (PCA). Then the scores of the principal compounds (PCs) were chosen as input nodes for input layer instead of the spectra data. The artificial neural networks (ANNs) models using the spectra data as input nodes were also established, which were compared with the PC-ANNs models. Four different preprocessing methods (first-derivation, second-derivation, standard normal variate (SNV) and multiplicative scatter correction (MSC)) were applied to NIR conventional spectra. The result shows the first-derivative model of PC-ANNs multivariate calibration has the lowest training errors and predicting errors. The concept of the degree of approximation was introduced and performed as the selective criterion of the optimum network parameters.