Application of artificial neural network to determination of active principle ingredient in pharmaceutical quality control based on near infrared spectroscopy

In the construction of a neural network, most attentions have been paid to the selection of the architecture, the selection of the learning parameters and the network validation while the selection of input variables shared little. This study focused on the selection of input variables by various data pre-treatment for constructing ANN models. The results showed that the validation results differed from each other when different data-pretreatment methods combined with near-infrared spectroscopy (NIRS) to build a model using artificial neural network (ANN) for quality control of paracetamol in coldrex. And wavelet coefficients after orthogonal signal correction (OSC) in the ANN models reduced RMSEP by up to 77% compared to ANN models using derivatives combined with PCA pretreatment. The selection of input variables has potent to improve the calibration ability of ANN, and the model can be used for pressure reduction of quality control in the pharmaceutical industry.