Application of near infrared diffuse reflectance spectroscopy with radial basis function neural network to determination of rifampincin isoniazid and pyrazinamide tablets

作者：Du Lin na^*; Wu Li hang; Gou Wei liang; Meng Qing fan; Jiang Chao jun; Shen Si le; Teng Li rong

来源：Chemical Research in Chinese Universities, 2007, 23(5): 518-523.

DOI：10.1016/S1005-9040(07)60113-5

• rifampicin isoniazid and pyrazinamide tablets
• NIR diffuse reflectance spectroscopy
• partial least square
• back-propagation neural network
• radial basis function neural network

Partial least squares( PLS ), back-propagation neural network( BPNN ) and. radial basis function neural network RBFNN) were respectively used for estalishing quantative analysis models with near infrared( NIR ) diffuse reflectance spectra for determining the contents of rifampincin ( RMP ), isoniazid ( INH ) and pyrazinamide ( PZA ) in rifampicin isoniazid and pyrazinamide tablets. Savitzky-Golay smoothing, first derivative, second derivative, fast Fourier transform( FFT ) and standard normal variate ( SNV ) transformation methods were applied to pretreating raw NIR diffuse reflectance spectra. The raw and pretreated spectra were divided into several regions, depending on the average spectrum and RSD spectrum. Principal component analysis( PCA ) method was used for analyzing the raw and pretreated spectra in different regions in order to reduce the dimensions of input data. The optimum spectral regions and the models' parameters were chosen by comparing the root mean square error of cross-validation( RMSECV ) values which were obtained by leave-one-out cross-validation method. The RMSECV values of the RBFNN models for determining the contents of RMP, INH and PZA were 0. 00288, 0. 00226 and 0. 00341, respectively. Using these models for predicting the contents of INH, RMP and PZA in prediction set, the RMSEP values were 0. 00266, 0. 00227 and 0. 00411, respectively. These results are better than those obtained from PIS models and BPNN models. With additional advantages of fast calculation speed and less dependence on the initial conditions, RBFNN is a suitable tool to model complex systems.

• 出版日期2007-9
• 单位吉林大学