Net analyte signal-artificial neural network (NAS-ANN) model for efficient nonlinear multivariate calibration

In recent years, artificial neural network (ANN) has been found major popularity in the analytical chemistry. This manuscript describes a simple and efficient ANN for modeling nonlinear spectral responses in spectroscopic multicomponent analyses. In this model, the spectral data were first subjected to net analyte signal (NAS) calculation and then the norm of the NAS vectors (||NAS||) was used as the input of the ANN model. Therefore, a simple model (NAS-ANN model) with only one node in input layer was obtained. A multilayer feed-forward neural network with back-propagation learning algorithm was used to process the nonlinear relationship between the ||NAS|| and concentration of analytes. The performance of the proposed model was evaluated by analysis of the simulated as well as the experimental data. In the simulated data, two source of nonlinearity including quadratic absorbance-concentration relationship and synergist effect were considered. In addition, the model was used for the simultaneous determination of three phenothiazine drugs including promethazine, chlorpromazine and perphenazine in their ternary mixture using conventional and derivative absorbance spectra. It was obtained that the proposed model could analyze the synthetic mixtures accurately. The model was compared with the PC-ANN model which is currently used for nonlinear multivariate calibration. The data confirmed that our model was simpler and produced more accurate results.