光学学报, 2019, 39 (2): 0230002, 网络出版: 2019-05-10  

基于分区建模的锌液痕量铜离子光谱检测方法 下载: 865次

A Spectrophotometric Detecting Method of Trace Copper Ion in Zinc Solution Based on Partition Modeling
作者单位
中南大学信息科学与工程学院, 湖南 长沙 410083
摘要
炼锌溶液中痕量铜离子的光谱信号被掩蔽、干扰严重,以及铜离子的非线性特性在高、低浓度区间的显著差异,都会导致痕量铜离子的浓度检测比较困难。针对该问题,提出了一种基于分区建模的锌液中痕量铜离子的光谱检测方法。该方法采用导数光谱结合小波去噪的方法对光谱信号进行预处理,重现待测铜离子的谱峰。以相关系数-稳定性值作为变量的评价指标对波长变量进行排序,并结合支持向量回归(SVR)模型选取最佳波长变量,在此基础上,根据混合溶液中铜离子光谱信号非线性特性将浓度划分区间,并分别针对每个区间建立粒子群优化支持向量回归(PSO-SVR)模型,计算出铜离子的质量浓度。将所提方法与现有多种回归方法进行比较,结果表明:所提方法将预测方均根误差降低至0.0678,模型决定系数提高至99.61%,该方法的最大相对误差为6.94%,平均相对误差为2.74%。
Abstract
The mass concentration detection of trace copper ion in zinc solution is difficult because of trace copper spectral signals masking, serious interference and significant nonlinearity difference of copper ion in the high and low mass concentration intervals. Aiming at this issue, we propose a spectrophotometric detecting method of trace copper ion in zinc solution based on partition modeling. The derivative spectrum combined with wavelet denoising is used to preprocess the spectral signal and reproduce the spectral peak of the copper ion to be measured. The wavelength variables are ranked by the correlation coefficient-stability value, which serves as the evaluation index of the variables, and the support vector regression (SVR) model is used to select the optimal wavelength variables. On this basis, the mass concentration of the copper ions is divided into several intervals according to the nonlinear characteristics in the mixed solution. The particle swarm optimization support vector regression (PSO-SVR) model is respectively established for each interval to compute the concentration of copper ions. The proposed method is compared with many existing regression methods. The results show that the predicted root mean square error obtained with the proposed method is reduced to 0.0678, and the model determination coefficient is increased to 99.61%. The maximum relative error obtained with the method is 6.94% and the average relative error is 2.74%.

朱红求, 吴书君, 李勇刚, 阳春华. 基于分区建模的锌液痕量铜离子光谱检测方法[J]. 光学学报, 2019, 39(2): 0230002. Hongqiu Zhu, Shujun Wu, Yonggang Li, Chunhua Yang. A Spectrophotometric Detecting Method of Trace Copper Ion in Zinc Solution Based on Partition Modeling[J]. Acta Optica Sinica, 2019, 39(2): 0230002.

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