Dou Ying, Mi Hong, Zhao Lingzhi, Ren Yuqiu, Ren Yulin
Department of Analytical Chemistry, College of Chemistry, Jilin University, Changchun 130021, China.
Anal Biochem. 2006 Apr 15;351(2):174-80. doi: 10.1016/j.ab.2005.10.041. Epub 2005 Nov 17.
A method for simultaneous, nondestructive analysis of aminopyrine and phenacetin in compound aminopyrine phenacetin tablets with different concentrations has been developed by principal component artificial neural networks (PC-ANNs) on near-infrared (NIR) spectroscopy. In PC-ANN models, the spectral data were initially analyzed by principal component analysis. Then the scores of the principal components were chosen as input nodes for the input layer instead of the spectral data. The artificial neural network models using the spectral data as input nodes were also established and compared with the PC-ANN models. Four different preprocessing methods (first-derivative, second-derivative, standard normal variate (SNV), and multiplicative scatter correction) were applied to three sets of NIR spectra of compound aminopyrine phenacetin tablets. The PC-ANNs approach with SNV preprocessing spectra was found to provide the best results. The degree of approximation was performed as the selective criterion of the optimum network parameters.
采用主成分人工神经网络（PC - ANNs）结合近红外（NIR）光谱技术，建立了一种可同时、无损分析不同浓度复方氨基比林非那西丁片中氨基比林和非那西丁的方法。在PC - ANN模型中，首先通过主成分分析对光谱数据进行分析。然后，选择主成分得分作为输入层的输入节点，而非光谱数据。同时建立了以光谱数据作为输入节点的人工神经网络模型，并与PC - ANN模型进行比较。对复方氨基比林非那西丁片的三组近红外光谱应用了四种不同的预处理方法（一阶导数、二阶导数、标准正态变量变换（SNV）和多元散射校正）。结果发现，采用SNV预处理光谱的PC - ANNs方法能提供最佳结果。以逼近度作为选择最佳网络参数的标准。
