基于分区建模的锌液痕量铜离子光谱检测方法
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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%。
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%.
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%.
引文
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[7] Liu X, Chen H C, Liu T A, et al. Application of PCA-SVR to NIR prediction model for tobacco chemical composition[J]. Spectroscopy and Spectral Analysis, 2007, 27(12): 2460-2463. 刘旭, 陈华才, 刘太昂, 等. PCA-SVR联用算法在近红外光谱分析烟草成分中的应用[J]. 光谱学与光谱分析, 2007, 27(12): 2460-2463.
[8] Zhu H Q, Gong J, Li Y G, et al. A spectrophotometric detecting method of trace cobalt under high concentrated zinc solution[J]. Spectroscopy and Spectral Analysis, 2017, 37(12): 3882-3888. 朱红求, 龚娟, 李勇刚, 等. 一种高锌背景下痕量钴离子浓度分光光度测量法[J]. 光谱学与光谱分析, 2017, 37(12): 3882-3888.
[9] Chen H Z, Pan T, Chen J M, et al. Waveband selection for NIR spectroscopy analysis of soil organic matter based on SG smoothing and MWPLS methods[J]. Chemometrics and Intelligent Laboratory Systems, 2011, 107(1): 139-146.
[10] Li C, Zhao T L, Li C, et al. Determination of gossypol content in cottonseeds by near infrared spectroscopy based on Monte Carlo uninformative variable elimination and nonlinear calibration methods[J]. Food Chemistry, 2017, 221: 990-996.
[11] Liu S S, Zhang J, Lin S H, et al. Quantitative analysis of copper element in pig feed using laser induced breakdown spectroscopy combined with CARS algorithm[J]. Laser & Optoelectronics Progress, 2018, 55(2): 023001. 刘珊珊, 张俊, 林思寒, 等. 激光诱导击穿光谱结合竞争自适应重加权采样算法对猪饲料中铜元素的定量分析[J]. 激光与光电子学进展, 2018, 55(2): 023001.
[12] Zhu H Q, Zou S N, Yang C H, et al. Simultaneously measuring method for Zn(II), Co(II) based on feature interval association-partial least squares[J]. Acta Optica Sinica, 2017, 37(6): 0630004. 朱红求, 邹胜男, 阳春华, 等. 基于特征区间联合-偏最小二乘的Zn(II)、Co(II)同时测量方法[J]. 光学学报, 2017, 37(6): 0630004.
[13] Chen Q S, Zhao J W, Zhang H D, et al. Indentification of authenticity of tea with near infrared spectroscopy based on support voctor machine[J]. Acta Optica Sinica, 2006, 26(6): 933-937. 陈全胜, 赵杰文, 张海东, 等. 基于支持向量机的近红外光谱鉴别茶叶的真伪[J]. 光学学报, 2006, 26(6): 933-937.
[14] Wang C L, Liu J G, Zhao N J, et al. Quantitative analysis of laser-induced breakdown spectroscopy of heavy metals in water based on support-vector-machine regression[J]. Acta Optica Sinica, 2013, 33(3): 0330002. 王春龙, 刘建国, 赵南京, 等. 基于支持向量机回归的水体重金属激光诱导击穿光谱定量分析研究[J]. 光学学报, 2013, 33(3): 0330002.
[15] Zhang Y Z, Liu Y, Hou H Y, et al. Intrinsic tissue fluorescence spectrum recovery based on particle swarm optimization algorithm[J]. Chinese Journal of Lasers, 2016, 43(5): 0504001. 张元志, 刘勇, 侯华毅, 等. 基于粒子群优化算法的生物组织固有荧光光谱复原方法[J]. 中国激光, 2016, 43(5): 0504001.
[2] Wang G W, Yang C H, Zhu H Q, et al. State-transition-algorithm-based resolution for overlapping linear sweep voltammetric peaks with high signal ratio[J]. Chemometrics and Intelligent Laboratory Systems, 2016, 151: 61-70.
[3] Xu D, Fan W, Lv H Y, et al. Simultaneous determination of traces amounts of cadmium, zinc, and cobalt based on UV-Vis spectrometry combined with wavelength selection and partial least squares regression[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2014, 123: 430-435.
[4] Zhi T X, Shang L P, Deng H, et al. Simultaneous determination of mixed systems using principal components regression by fluorescence spectroscopy[J]. Applied Chemical Industry, 2008, 37(10): 1231-1234. 职统兴, 尚丽平, 邓琥, 等. 主成分回归荧光光谱法同时分析多组分混合体系[J]. 应用化工, 2008, 37(10): 1231-1234.
[5] Zhang C H, Yun Y H, Zhang Z M, et al. Simultaneous determination of neutral and uronic sugars based on UV-vis spectrometry combined with PLS[J]. International Journal of Biological Macromolecules, 2016, 87: 290-294.
[6] Hu Y, Li Z H, Lü T. Quantitative measurement of iron content in geological standard samples by laser-induced breakdown spectroscopy combined with artificial neural network[J]. Laser & Optoelectronics Progress, 2017, 54(5): 053003. 胡杨, 李子涵, 吕涛. 激光诱导击穿光谱结合人工神经网络测定地质标样中的铁含量[J]. 激光与光电子学进展, 2017, 54(5): 053003.
[7] Liu X, Chen H C, Liu T A, et al. Application of PCA-SVR to NIR prediction model for tobacco chemical composition[J]. Spectroscopy and Spectral Analysis, 2007, 27(12): 2460-2463. 刘旭, 陈华才, 刘太昂, 等. PCA-SVR联用算法在近红外光谱分析烟草成分中的应用[J]. 光谱学与光谱分析, 2007, 27(12): 2460-2463.
[8] Zhu H Q, Gong J, Li Y G, et al. A spectrophotometric detecting method of trace cobalt under high concentrated zinc solution[J]. Spectroscopy and Spectral Analysis, 2017, 37(12): 3882-3888. 朱红求, 龚娟, 李勇刚, 等. 一种高锌背景下痕量钴离子浓度分光光度测量法[J]. 光谱学与光谱分析, 2017, 37(12): 3882-3888.
[9] Chen H Z, Pan T, Chen J M, et al. Waveband selection for NIR spectroscopy analysis of soil organic matter based on SG smoothing and MWPLS methods[J]. Chemometrics and Intelligent Laboratory Systems, 2011, 107(1): 139-146.
[10] Li C, Zhao T L, Li C, et al. Determination of gossypol content in cottonseeds by near infrared spectroscopy based on Monte Carlo uninformative variable elimination and nonlinear calibration methods[J]. Food Chemistry, 2017, 221: 990-996.
[11] Liu S S, Zhang J, Lin S H, et al. Quantitative analysis of copper element in pig feed using laser induced breakdown spectroscopy combined with CARS algorithm[J]. Laser & Optoelectronics Progress, 2018, 55(2): 023001. 刘珊珊, 张俊, 林思寒, 等. 激光诱导击穿光谱结合竞争自适应重加权采样算法对猪饲料中铜元素的定量分析[J]. 激光与光电子学进展, 2018, 55(2): 023001.
[12] Zhu H Q, Zou S N, Yang C H, et al. Simultaneously measuring method for Zn(II), Co(II) based on feature interval association-partial least squares[J]. Acta Optica Sinica, 2017, 37(6): 0630004. 朱红求, 邹胜男, 阳春华, 等. 基于特征区间联合-偏最小二乘的Zn(II)、Co(II)同时测量方法[J]. 光学学报, 2017, 37(6): 0630004.
[13] Chen Q S, Zhao J W, Zhang H D, et al. Indentification of authenticity of tea with near infrared spectroscopy based on support voctor machine[J]. Acta Optica Sinica, 2006, 26(6): 933-937. 陈全胜, 赵杰文, 张海东, 等. 基于支持向量机的近红外光谱鉴别茶叶的真伪[J]. 光学学报, 2006, 26(6): 933-937.
[14] Wang C L, Liu J G, Zhao N J, et al. Quantitative analysis of laser-induced breakdown spectroscopy of heavy metals in water based on support-vector-machine regression[J]. Acta Optica Sinica, 2013, 33(3): 0330002. 王春龙, 刘建国, 赵南京, 等. 基于支持向量机回归的水体重金属激光诱导击穿光谱定量分析研究[J]. 光学学报, 2013, 33(3): 0330002.
[15] Zhang Y Z, Liu Y, Hou H Y, et al. Intrinsic tissue fluorescence spectrum recovery based on particle swarm optimization algorithm[J]. Chinese Journal of Lasers, 2016, 43(5): 0504001. 张元志, 刘勇, 侯华毅, 等. 基于粒子群优化算法的生物组织固有荧光光谱复原方法[J]. 中国激光, 2016, 43(5): 0504001.