Kai Xiao-Ming, Shen Yu-Hua, Zhang Gu-Xin, Xie An-Jian
Chemistry Department of Anqing Normal College, Anqing 246011, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2005 Dec;25(12):2070-2.
By means of artificial neural network and Levenberg Marquardt Back Propagation (LM-BP) train algorithm, the three components of pyrocatechol, resorcinol and hydroquinone were determined simultaneously. The absorption spectra of these three components severely overlap in ultraviolet spectral range. Three wavelengths at 283.5, 279.5 and 276.5 nm were selected for the determination. 25 mixture standard solutions were prepared according to orthogonal projection form L25 (5(6)). Three kinds of components were trained. Mean Squared Error (MSE) reaching minimum value is 0.083 114 3. Meanwhile the contents of pyrocatechol, resorcinol and hydroquinone in six simulation mixture samples were predicted. The relative errors of the three kinds of components were slightly larger under the low concentration condition, and the mean relative error for most analytical results was less than 5%, especially it is satisfactory for the analytical results of pyrocatechol and resorcinol with severely overlapped absorption spectra.
采用人工神经网络和Levenberg Marquardt反向传播(LM-BP)训练算法,同时测定了邻苯二酚、间苯二酚和对苯二酚三种组分。这三种组分的吸收光谱在紫外光谱范围内严重重叠。选择283.5、279.5和276.5 nm三个波长进行测定。按照L25(5(6))的正交设计表配制了25份混合标准溶液。对三种组分进行了训练,均方误差(MSE)达到的最小值为0.083 114 3。同时对6个模拟混合样品中邻苯二酚、间苯二酚和对苯二酚的含量进行了预测。三种组分在低浓度条件下相对误差稍大,多数分析结果的平均相对误差小于5%,对于吸收光谱严重重叠的邻苯二酚和间苯二酚的分析结果尤其令人满意。