Application of GA-RBF networks to the nondestructive determination of active component in pharmaceutical powder by NIR spectroscopy

作者：Qu Nan; Mi Hong; Wang Bin; Ren Yulin^*

来源：Journal of the Taiwan Institute of Chemical Engineers, 2009, 40(2): 162-167.

DOI：10.1016/j.jtice.2008.08.002

• Genetic algorithm
• RBF neural network
• Wavelength selection
• Near-infrared spectroscopy
• Nondestructive quantitative analysis

A new method was applied to nondestructive quantitative analysis of pharmaceutical samples with different concentrations on the basis of the near-infrared spectral data. By the proposed method powerful radial basis RBF) networks can be produced based on genetic algorithm (GA), which is applied to auto-configuring the structure of the networks to optimize the near-infrared wavelength regions used and variables employed in building radial basis function networks. Estimation and calibration of the sample concentration via NIR spectroscopy were made with the aid of genetic algorithm-radial basis GA-RBF) network models based on conventional spectra, standard normal variate (SNV), multiplicative scatter correction (MSC) and the first-derivative spectra, various optimum models of which were established and compared. Experiment results show that GA-RBF networks can give robust and satisfactory prediction and the GA-RBF model based oil SNV preprocessing spectra was found to provide the best performance. Therefore, the proposed method may have significant potential for use in nondestructive quantitative analysis of pharmaceutical samples.

• 出版日期2009-3
• 单位吉林农业大学; 吉林大学; 吉林省中医药科学院