Calibration in non-linear NIR spectroscopy using principal component artificial neural networks

Near-infrared (NIR) spectroscopy was used in simultaneous, non-destructive analysis of antipyriine and caffeine citrate tablets. Principal component artificial neural networks (PC-ANNs) were used to construct models for the analytes, using the testing set for external validation. Four pretreated spectra, namely, first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC) spectra led to simplified and more robust models than conventional spectra. In PC-ANNs models, the spectra data were analyzed by principal component analysis (PCA) firstly. 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. The result shows the SNV model of PC-ANNs multivariate calibration has the lowest training error and predicting error. The concept of the degree of approximation was introduced and performed as the selective criterion of the optimum network parameters.