Non-destructive determination of metronidazole powder by using artificial neural networks on short-wavelength NIR spectroscopy

作者：Zhao Lingzhi; Dou Ying; Mi Hong; Ren Meiyan; Ren Yulin^*

来源：Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy , 2007, 66(4-5): 1327-1332.

DOI：10.1016/j.saa.2006.06.022

• short-wavelength NIR spectroscopy
• artificial neural networks
• preprocessing
• metronidazole powder

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 780-1100nm 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.

• 出版日期2007-4
• 单位吉林大学; 吉林省中医药科学院; 天津大学