Nondestructive quantitative analysis of erythromycin ethylsuccinate powder drug via short-wave near-infrared spectroscopy combined with radial basis function neural networks.

A new assay method for the nondestructive determination of erythromycin ethylsuccinate powder drug via short-wave near-infrared spectroscopy (NIR) combined with radial basis function (RBF) neural networks is investigated. The modern near-infrared spectroscopy analysis technique is efficient, simple and nondestructive, which has been used in chemical analysis in diverse fields. Short-wave NIR is a more rapid, flexible, and cost-effective method to control product concentration in pharmaceutical industry. The RBF neural networks are local approximation networks that have superiorities in function approximation and learning speed. In addition, the structure of RBF networks is simple. Estimate and calibration of the sample concentration via short-wave NIR are made with the aid of RBF models based on conventional spectra, standard normal variate (SNV), multiplicative scatter correction (MSC) and the first-derivative spectra. Various optimum models of them are established and compared. Experiment results show that the models of SNV spectra can give better performance, and the optimized RBF neural network model after SNV treatment were given, by which the root-mean-square-errors (RMSE) for calibration set and test set were 0.3266% and 0.5244%, respectively and the correlation coefficients (R) for calibration set and test set were 0.9942 and 0.9852, respectively. The proposed RBF method based on short-wave NIR is more valuable and economical for quantitative analysis than traditional methods such as partial least squares (PLS).