Non-destructive determination of metronidazole powder by using artificial neural networks on short-wavelength NIR spectroscopy.
The present study aimed at providing a new method in sight into short-wavelength near-infrared (NIR) spectroscopy of in pharmaceutical quantitative analysis. To do that, 124 experimental samples of metronidazole powder were analyzed using artificial neural networks (ANNs) in the 780-1100 nm region of short-wavelength NIR spectra. In this paper, metronidazole was as active component and other two components (magnesium stearate and starch) were as excipients. Different preprocessing spectral data (first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC)) were applied to establish the ANNs models of metronidazole powder. The degree of approximation, a new evaluation criterion of the networks was employed to prove the accuracy of the predicted results. The results presented here demonstrate that the short-wavelength NIR region is promising for the fast and reliable determination of major component in pharmaceutical analysis.
Zhao
Lingzhi
L
College of Chemistry, Jilin University, Changchun 130021, China.
Dou
Ying
Y
Mi
Hong
H
Ren
Meiyan
M
Ren
Yulin
Y
D008795:Metronidazole / Q000032:analysis*; D016571:Neural Networks, Computer; D011208:Powders / Q000032:analysis*; D019265:Spectroscopy, Near-Infrared
D011208:Powders; D008795:Metronidazole