Radial basis function networks combined with genetic algorithm applied to nondestructive determination of compound erythromycin ethylsuccinate powder

A new assay method for the nondestructive determination of pharmaceutical samples with different concentrations on the basis of the near-infrared (NIR) spectral data is presented in this paper. By the proposed method, powerful radial basis function (RBF) networks can be produced based on a genetic algorithm (GA), which is applied for auto-configuring the structure of the networks and obtaining the optimal network parameters. The Akaike's information criterion (AIC) is used to evaluate the fitness of individual networks. Therefore, the genetic algorithm-radial basis function (GA-RBF) networks have a better generalization performance and simpler network structure. Four different GA-RBF network models based on pretreated spectra (multiplicative scatter correction MSC, standard normal variate SNV, first-derivative and second-derivative spectra) have been established and compared. The obtained GA-RBF networks can give robust and satisfactory prediction and the optimal GA-RBF networks after the SNV treatment is found to provide the best results. It is demonstrated that the proposed GA-RBF method based on NIR spectral data is a valuable tool for quantitative analysis.