神经网络显色光度法用于镧系稀土的同时测定

NEURAL NETWORKS AND CHELATING SPECTROPHOTOMETRY FOR SIMULTANEOUS DETERMINATION OF FIFTEEN RARE EARTH ELEMENTS

研究了神经网络（ＮＮ）反传算法及其在光谱分辨中的应用．借三溴偶氮氯膦（ＴＢＣＰＡ）为显色剂，以光度法同时测定１５种稀土元素，相对误差一般小于５％（ＲＳＤ≤５％），表明结果良好．

Neural networks(NN) and multiwavelength spectroscopy were investigated systematically for multicomponent analysis. In this paper, neural networks and chelating spectrophotometry were applied to multivariate calibration and spectral resolution. The neural networks(NN) were trained by ordinary backpropagation(OBP) and the modified backpropagation(MBP). The chelating photometric reagent was selected as tribromochlorophosphanazo(TBCPA). The proposed senaitive spectrophotometry combined with the backpropagation neural networks (BPNN) was used for the simultaneous determination of 15 rare earth elements with good results. The relative standard divrations were leas than and about equal to 5%, RSD≤5%, in general. It was shown that the neural networks can be used as a novel powerful chemometric technique for multicomponent analysis, especially for multivariate calibration and spectral resolution.