[人工神经网络在铜、钴和镍同时分光光度测定中的应用]
[Application of artificial neural network to simultaneous spectrophotometric determination of Cu, Co and Ni].
作者信息
He C Y, Sun Y M, Wu G H, Chen R
机构信息
Department of Chemistry, Anqing Normal College, Anqing 246011, China.
出版信息
Guang Pu Xue Yu Guang Pu Fen Xi. 2001 Oct;21(5):719-22.
By means of artificial neural network and Levenberg-Marquardt back-propagation train algorithm, the three-component metal coordinate compounds of PAR-Cu, Co, Ni were determined simultaneously, in which the spectra overlapped. In 452-552 nm, the absorbance(A) at 14 wavelength were taken as character of artificial neural network, and samples were arranged by method of orthogonal design. The mean recovery of Cu, Co, Ni were 99.96%, 99.99% and 99.97% respectively. The RSD of the results were 0.1%, 0.2% and 0.1% respectively. The results were better than those of other networks in training speed and the accuracy. In conclusion, the new network spectrophotometry is a good choice for resolving multicomponent.
采用人工神经网络和Levenberg-Marquardt反向传播训练算法，对PAR-Cu、Co、Ni三元金属配合物进行同时测定，其中光谱存在重叠。在452 - 552 nm范围内，取14个波长处的吸光度(A)作为人工神经网络的特征量，并采用正交设计法对样品进行排列。Cu、Co、Ni的平均回收率分别为99.96%、99.99%和99.97%。结果的相对标准偏差分别为0.1%、0.2%和0.1%。在训练速度和准确性方面，该结果优于其他网络。综上所述，新型网络分光光度法是解决多组分问题的良好选择。
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