基于特征区间联合-偏最小二乘的Zn(II)、Co(II)同时测量方法

针对Zn(II)、Co(II)混合溶液的紫外可见(UV-Vis)吸收光谱重叠严重、难以分离的问题, 提出了一种基于特征区间联合-偏最小二乘的Zn(II)、Co(II)同时测量方法。针对混合溶液在400~800 nm波长段的吸收光谱, 利用特征区间联合法以分区的方式对Zn(II)、Co(II)的特征区间进行筛选, 并以留一交叉验证均方根误差VRMSECV最小和决定系数R2最大挑选出Zn(II)、Co(II)的最优特征区间; 再联合这些最优子区间建立偏最小二乘(PLS)模型, 从而获得Zn(II)、Co(II)离子浓度。结果证明, 该方法不仅能降低波长筛选的复杂度, 还能保证波长筛选过程的稳定性, 从而将Zn(II)模型的VRMSECV及预测平均相对误差降低到0.0483和3.48%, Co(II)模型的VRMSECV及预测平均相对误差降低到0.0501和4.25%, 并将Zn(II)、Co(II)模型R2值提高到99.41%和99.22%; 同时, 还可以将光谱仪的Zn(II)、Co(II)扫描波段固定在所选的特征区段, 大幅提高光谱检测效率。

Abstract

A simultaneously measuring method for Zn(II)、Co(II) based on feature interval association-partial least squares is proposed to solve the problem that the ultraviolet visible (UV-Vis) absorption spectrum of mixed solution with Zn(II), Co(II) is seriously overlapped and difficult to separate. For the absorption spectrum in the range of 400~800 nm of mixed solution, the method of feature interval association is used to select the characteristic interval of Zn(II) and Co(II) in the way of partition, and the optimal feature interval of Zn(II) and Co(II) is selected by the minimum root mean square error of cross validation VRMSECV and the maximum determination coefficient R2. Finally, these optimal intervals are associated to establish partial least squares (PLS) model, and ion concentrations of Zn(II) and Co(II) are obtained. The result indicates that the proposed method can not only reduce the complexity of the wavelength selection, but also ensure the stability of the wavelength selection process. Thus, the VRMSECV and the predicted average relative error of Zn(II) model are reduced to 0.0483 and 3.48%. The VRMSECV and the predicted average relative error of Co(II) model are reduced to 0.0501 and 4.25%. And the R2 of Zn(II) and Co(II) are increased to 99.41% and 99.22%. In addition, the application of the method can fix the scanning spectrum of Zn(II) and Co(II) in the selected feature intervals, which greatly improves the efficiency of spectrum detection.

参考文献

[1] 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.

[2] 邰玲, 弓巧娟, 孙鸿, 等. 荧光光谱法在水质监测中的应用［J］. 光谱实验室, 2011, 28(2): 940-945.

Tai Ling, Gong Qiaojuan, Sun Hong, et al. Application of water quality monitoring by fluorescence spectrometry［J］. Chinese Journal of Spectroscopy Laboratory, 2011, 28(2): 940-945.

[3] 靳兰兰, 王秀季, 李会来, 等. 电感耦合等离子体质谱技术进展及其在冶金分析中的应用［J］. 冶金分析, 2016, 36(7): 1-14.

Jin Lanlan, Wang Xiuji, Li Huilai, et al. Progress in inductively coupled plasma mass spectrometry technology and its application in metallurgical analysis［J］. Metallurgical Analysis, 2016, 36(7): 1-14.

[4] Wang G W, Yang C H, Li Y G, et al. Reagent optimization for on-line simultaneous polarographic determination of trace amounts of Cu2+, Cd2+ and Co2+ in the presence of anextremely large excess of Zn2+［J］. Journal of Central South University, 2016, 23(9): 2199-2204.

[5] 汤斌, 魏彪, 毛本将, 等. 紫外-可见吸收光谱法水质检测系统的噪声分析与处理研究［J］. 激光与光电子学进展, 2014, 51(4): 043002.

Tang Bin, Wei Biao, Mao Benjiang, et al. Noise analysis and denoising research on the UV-visible absorption spectroscopy water quality detection system［J］. Laser & Optoelectronics Progress, 2014, 51(4): 043002.

[6] 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.

[7] 白玲, 倪永年. 偏最小二乘分光光度法同时测定痕量铁、锰、铜、锌、钴和镍［J］. 分析试验室, 2002, 21(1): 39-42.

Bai Ling, Ni Yongnian. Simultaneous spectrophotometric determination of trace iron, manganese, copper, zinc, cobalt and nickel by partial least squares method［J］. Chinese Journal of Analysis Laboratory, 2002, 21(1): 39-42.

[8] 李硕, 汪善勤, 张美琴. 基于可见-近红外光谱比较主成分回归、偏最小二乘回归和反向传播神经网络对土壤氮的预测研究［J］. 光学学报, 2012, 32(8): 0830001.

Li Shuo, Wang Shanqin, Zhang Meiqin. Comparision among principal component regression, partical least squares regression and back propagatiom neural network for prediction of soil nitrogen with visible-near infrared spectroscopy［J］. Acta Optica Sinica, 2012, 32(8): 0830001.

[9] 陈孝敬, 吴迪, 虞佳佳, 等. 一种用于可见-近红外光谱特征波长选择的新方法［J］. 光学学报, 2008, 28(11): 2153-2158.

Chen Xiaojing, Wu Di, Yu Jiajia, et al. A new choice method of characteristic wavelength of visible/near infrared spectroscopy［J］. Acta Optica Sinica, 2008, 28(11): 2153-2158.

[10] 彭海根, 彭云发, 詹映, 等. 近红外光谱技术联合区间间隔偏最小二乘法对南疆红枣糖度的测定［J］. 食品科技, 2014, 39(6): 276-279.

Peng Haigen, Peng Yunfa, Zhan Ying, et al. Determination of the sugar content of jujube in south Xinjiang by near infrared spectroscopy combined with siPLS methods［J］. Food Science and Technology, 2014, 39(6): 276-279.

[11] 武小红, 孙俊, 武斌, 等. 基于联合区间偏最小二乘判别分析的猪肉近红外光谱定性建模分析［J］. 激光与光电子学进展, 2015, 52(4): 043003.

Wu Xiaohong, Sun Jun, Wu Bin, et al. Qualitative analysis model of near infrared spectra of pork based on synergy interval partial least squares discriminant analysis［J］. Laser & Optoelectronics Progress, 2015, 52(4): 043003.

[12] 郝勇, 孙旭东, 潘圆媛, 等. 蒙特卡罗无信息变量消除方法用于近红外光谱预测果品硬度和表面色泽的研究［J］. 光谱学与光谱分析, 2011, 31(5): 1225-1229.

Hao Yong, Sun Xudong, Pan Yuanyuan, et al. Detection of firmness and surface color of pear by near infrared spectroscopy based on Monte Carlo uninformative variables elimination method［J］. Spectroscopy and Spectral Analysis, 2011, 31(5): 1225-1229.

[13] 李江波, 郭志明, 黄文倩, 等. 应用CARS和SPA算法对草莓SSC含量NIR光谱预测模型中变量及样本筛选［J］. 光谱学与光谱分析, 2015, 35(2): 372-378.

Li Jiangbo, Guo Zhiming, Huang Wenqian, et al. Near-infrared spectra combining with CARS and SPA algorithms to screen the variables and samples for quantitatively determining the soluble solids content in strawberry［J］. Spectroscopy and Spectral Analysis, 2015, 35(2): 372-378.

[14] 付元元, 王纪华, 杨贵军, 等. 应用波段深度分析和偏最小二乘回归的冬小麦生物量高光谱估算［J］. 光谱学与光谱分析, 2013, 32(5): 1315-1319.

Fu Yuanyuan, Wang Jihua, Yang Guijun, et al. Band depth analysis and partial least square regression based winter wheat biomass estimation using hyperspectral measurements［J］. Spectroscopy and Spectral Analysis, 2013, 32(5): 1315-1319.

[15] 邹小波, 赵杰文. 用遗传算法快速提取近红外光谱特征区域和特征波长［J］. 光学学报, 2007, 27(7): 1316-1321.

Zou Xiaobo, Zhao Jiewen. Methods of characteristic wavelength region and wavelength selection based on genetic algorithm［J］. Acta Optica Sinica, 2007, 27(7): 1316-1321.

朱红求, 邹胜男, 阳春华, 李勇刚, 陈俊名. 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. Zhu Hongqiu, Zou Shengnan, Yang Chunhua, Li Yonggang, Chen Junming. Simultaneously Measuring Method for Zn(II)、Co(II) Based on Feature Interval Association-Partial Least Squares[J]. Acta Optica Sinica, 2017, 37(6): 0630004.

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