Artificial neural networks for non-destructive determination of acetylspiramycin powder by short-wavelength NIR spectroscopy

The present study has aimed at providing new insight into short-wavelength near-infrared (SW-NIR) spectroscopy (780–1100 nm) for non-destructive quantitative analysis of acetylspiramycin (macrolide antibiotics) powder by using artificial neural networks (ANNs). Presently, it was shown the third vibrational overtone of the CH stretching band can be used to quantitatively determine constituents in pharmaceutical. The third overtone referred to as the SW-NIR region ranges from 780 nm to 1100 nm. In this paper, 156 experimental samples of acetylspiramycin powder were analyzed using ANNs in the 780–1100 nm region of SW-NIR spectra. Four different pretreated methods (first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC)) were applied to three sets of SW-NIR spectra of powder samples. The results presented here demonstrate that the SW-NIR region is promising for the fast and reliable determination of major component in pharmaceutical analysis. Degree of approximation as an evaluation criterion of the network was employed, which proved the accuracy of the predicted results.