Wu Gen-hua, He Chi-yang, Chen Rong
Department of Chemistry, Anqing Normal College, Anqing 246011, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2003 Apr;23(2):318-21.
A new fluorescence method by artificial neural network for the simultaneous determination of tryptophan and tyrosine was developed. The determination was carried out in the KH2PO4-K2HPO4 buffer solution (pH = 7.15) and at the EX (excitation) wavelength of 224 nm. In the range of 290-400 nm, the fluorescence intensities at fourteen wavelengths were taken as characteristic parameters of the artificial neural network, and the samples were arranged by the method of equality design. The mean recoveries of tryptophan and tyrosine were 100.9% and 101.6% respectively. The RSDs of the results were 4.18% and 4.17%. The method has been applied to the determination of tryptophan and tyrosine in compound amino acid injection, and the relative errors were 4.0% and 2.6%, respectively. The results were better than those of other networks in training speed and accuracy. In conclusion, the new network spectrofluorimetry is a good choice for multicomponent resolving analysis.
建立了一种基于人工神经网络的同时测定色氨酸和酪氨酸的新型荧光方法。测定在KH2PO4-K2HPO4缓冲溶液(pH = 7.15)中进行,激发波长为224 nm。在290-400 nm范围内,选取14个波长处的荧光强度作为人工神经网络的特征参数,采用均匀设计法对样品进行排列。色氨酸和酪氨酸的平均回收率分别为100.9%和101.6%。结果的相对标准偏差分别为4.18%和4.17%。该方法已应用于复方氨基酸注射液中色氨酸和酪氨酸的测定,相对误差分别为4.0%和2.6%。在训练速度和准确性方面,结果优于其他网络。总之,新型网络荧光分析法是多组分解析分析的良好选择。