荧光光谱-人工神经网络法同时测定注射液中色氨酸和酪氨酸

本文用人工神经网络法中的误差反传学习算法对色氨酸、酪氨酸混合体系的荧光光谱进行解析,提出了同时测定这两种氨基酸的计算分析方法。以pH=7.15的KH_2PO_4-K_2HPO_4的缓冲溶液为介质,用224nm为激发波长,在290～400nm的范围内,以14个特征波长处的荧光强度值作为网络特征参数,进行网络训练。网络训练了12次即达到误差精度要求(误差平方和小于0.1)。以同样方法对复合氨基酸注射液进行测定,通过训练好的网络进行色氨酸、酪氨酸含量的计算,相对误差分别为4.0％和2.6％。实验表明,该方法与现有的算法相比具有训练速度快、预测结果准确度高等特点。该方法与荧光法结合有望成为多组分分析的有效方法之一。

Simultaneous Determination of Tryptophan and Tyrosine in Injection by Artificial Neural Network and Fluorescence Spectrum Method

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 intensies 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% and4.17%. 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.