共识独立成分回归结合紫外光谱用于多组分同时测定

Use of consensus independent component regression on ultraviolet spectra for simultaneous determination of multi-components

将独立成分分析用于紫外光谱定量分析,结合多模型共识的基本思想,建立了共识独立成分回归方法。从训练集随机取样建立一系列独立成分回归模型,选取其中性能较好的部分模型作为成员模型,并用这些成员模型预测未知样品。用该方法对苯甲酸、苯胺及苯酚3组分水溶液的紫外光谱进行分析,并与单模型偏最小二乘法了进行比较。结果PLSR对独立测试集中3种组分进行50次重复预测的平均RMSEP分别为2.349,7.413和1.605,RMSEP的标准偏差分别为1.781,2.918和1.266;而本方法重复50次预测的平均RMSEP分别为1.633,3.390和1.496,RMSEP的标准偏差分别为6.642×10^(-3),6.573×10^(-2)和4.484×10^(-2)。可见,共识独立成分回归所建立的模型更加稳健和可靠,预测的准确性也明显提高。

The independent component analysis was used for ultraviolet spectrum quantitative analysis, and a consensus independent component regression （clCR） method was proposed based on the basic idea of consensus modeling. A series of ICR models was built on training subsets which were constructed by random sampling from the training set; the models with high performance were selected as member models, and were used for prediction. The clCR was used for modeling on ultraviolet spectroscopic data which derived from a series of tri-component aqueous solution of benzoic acid, aniline and phenol. Meanwhile, the method was compared with the single-model partial least squares regression （PLSR）. As results, the single-model PLSR obtained 2.349, 7.413 and 1.605 of mean RMSEP on 50 repeat prediction for the three components on the independent test set, the standard deviation of the RMSEPs were 1.781, 2.918 and 1.266, respectively. While clCR obtained 1.633, 3.390 and 1.496 of mean RMSEP and 6.642×10^-3, 6.573×10^-2 and 4.484×10^-2 of correspond standard deviations. The results shown that the models built by clCR are more steady and reliable; and the prediction results are more accurate than single-model PLSR.