锌溶液中痕量Cu~(2+)、Co~(2+)的检测光谱预处理方法
|
摘要
针对同时检测锌溶液中痕量Cu~(2+)、Co~(2+)浓度存在的灵敏度低、有效波段窄、光谱信号覆盖严重的问题,提出了一种多目标优化分数阶微分预处理方法。首先根据光谱特点确定影响Cu~(2+)、Co~(2+)同时检测的覆盖度和失真度,并拟合微分阶次与指标的函数关系、约束条件,然后基于多目标粒子群优化算法求解,最后对多目标优化微分阶数方法进行验证。结果表明:所提方法可以重构完全被覆盖的低灵敏度、窄有效波段的离子波峰,解决光谱信号被完全覆盖的问题,并在最大程度降低求导滤波的失真度,降低Cu~(2+)、Co~(2+)的光谱覆盖率。
As for the simultaneous detection of trace Cu~(2+) and Co~(2+) in zinc solution, there exist the problems of low sensitivity, narrow effective band and serious spectral signal coverage. Thus, a multi-objective optimization fractional differentiation pretreatment method is proposed. First, the coverage degree and distortion degree in the simultaneous detection of Cu~(2+) and Co~(2+) are determined according to the spectral characteristics, and the functional relationship and constraints of differential order and index are fitted. Then, the established optimization problem is solved by the multi-objective particle swarm optimization algorithm. This multi-objective differential order optimization method is finally verified. The results show that the proposed method can be used to reconstruct the completely covered ion wave peaks with low sensitivity and narrow effective bands, and to solve the complete spectral coverage problem. Moreover, it can be used to minimize the distortion degree of differential filtering and reduce the spectral coverage of trace Cu~(2+)and Co~(2+)to the maximum extent.
As for the simultaneous detection of trace Cu~(2+) and Co~(2+) in zinc solution, there exist the problems of low sensitivity, narrow effective band and serious spectral signal coverage. Thus, a multi-objective optimization fractional differentiation pretreatment method is proposed. First, the coverage degree and distortion degree in the simultaneous detection of Cu~(2+) and Co~(2+) are determined according to the spectral characteristics, and the functional relationship and constraints of differential order and index are fitted. Then, the established optimization problem is solved by the multi-objective particle swarm optimization algorithm. This multi-objective differential order optimization method is finally verified. The results show that the proposed method can be used to reconstruct the completely covered ion wave peaks with low sensitivity and narrow effective bands, and to solve the complete spectral coverage problem. Moreover, it can be used to minimize the distortion degree of differential filtering and reduce the spectral coverage of trace Cu~(2+)and Co~(2+)to the maximum extent.
引文
[1] Lobinski R, Marczenko Z. Recent advances inultraviolet-visible spectrophotometry[J].Critical Reviews in Analytical Chemistry, 1992, 23(1/2):55-111.
[2] Zhu H Q, Chen J M, Yin D H, et al. A UV-Vis analytical method for polymetallic solutions[J].Journal of Chemical Industry and Engineering(China),2017, 68(3):998-1004.朱红求,陈俊名,尹冬航,等.一种基于紫外可见光谱的多金属离子浓度检测方法[J].化工学报,2017,68(3):998-1004.
[3] Christensen O. An introduction to wavelet analysis[J].Applied&Numerical Harmonic Analysis, 2010, 5(3):19-30.
[4] Mu L, Wang H H, Zhang Y H. An improved wavelet threshold denoising algorithm for analysing signals in the XRD spectrum[J]. Advances in Applied Mathematics, 2015, 4(3):224-229.牟丽,王海辉,张育浩.小波阈值去噪算法在XRD图谱去噪中的应用[J].应用数学进展,2015, 4(3):224-229.
[5] Tverdohleb J, Limarev I, Dubrovin V, et al.Wavelet analysis of complex nonstationary oscillatory signals[C]. 4th International Scientific-Practical Conference Problems of Infocommunications, Science and Technology(PIC S&T), 2017:12-20.
[6] Li B X, Wang L, Dong X R. Application of second derivative pretreatment in quantitative analysis using mid-infrared spectrum[J]. Chinese Journal of Analysis Laboratory, 2008, 27(7):9-12.李本祥,王玲,董新荣.二阶导数预处理法在中红外光谱定量分析中的应用研究[J].分析试验室,2008,27(7):9-12.
[7] Yang X K, Zhong M L, Jing X J, et al. FTIR microscopic image analysis based on principal component analysis-2~(nd)derivative spectral imaging[J]. Acta Optica Sinica, 2012, 32(7):0711001.杨秀坤,钟明亮,景晓军,等.基于主成分分析-二阶导数光谱成像的红外显微图像分析[J].光学学报,2012, 32(7):0711001.
[8] Zhang D, Tashpolat·Tiyip, Zhang F, et al.Application of fractional differential in preprocessing hyperspectral data of saline soil[J]. Transactions of the Chinese Society of Agricultural Engineering,2014, 30(24):151-160.张东,塔西甫拉提·特依拜,张飞,等.分数阶微分在盐渍土高光谱数据预处理中的应用[J].农业工程学报,2014, 30(24):151-160.
[9] Cai J H, Xiao Y L, Li X Q. De-noising of near infrared spectra based on generalized S transform and singular value decomposition[J]. Acta Optica Sinica,2018, 38(4):0430005.蔡剑华,肖永良,黎小琴.基于广义S变换和奇异值分解的近红外光谱去噪[J].光学学报,2018, 38(4):0430005.
[10] Xu P, Xiao C, Zhang J C, et al. Denoising method for plant hyperspectral data based on grouped 3D discrete cosine transform dictionary[J]. Acta Optica Sinica, 2017, 37(6):0630003.徐平,肖冲,张竞成,等.基于分组三维离散余弦变换字典的植物高光谱数据去噪方法[J].光学学报,2017, 37(6):0630003.
[11] Coello C A, Lechuga M S. MOPSO:a proposal for multiple objective particle swarm optimization[C].Proceedings of the 2002 Congress on Evolutionary Computation, 2002:1051-1056.
[12] 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.
[13] 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.
[2] Zhu H Q, Chen J M, Yin D H, et al. A UV-Vis analytical method for polymetallic solutions[J].Journal of Chemical Industry and Engineering(China),2017, 68(3):998-1004.朱红求,陈俊名,尹冬航,等.一种基于紫外可见光谱的多金属离子浓度检测方法[J].化工学报,2017,68(3):998-1004.
[3] Christensen O. An introduction to wavelet analysis[J].Applied&Numerical Harmonic Analysis, 2010, 5(3):19-30.
[4] Mu L, Wang H H, Zhang Y H. An improved wavelet threshold denoising algorithm for analysing signals in the XRD spectrum[J]. Advances in Applied Mathematics, 2015, 4(3):224-229.牟丽,王海辉,张育浩.小波阈值去噪算法在XRD图谱去噪中的应用[J].应用数学进展,2015, 4(3):224-229.
[5] Tverdohleb J, Limarev I, Dubrovin V, et al.Wavelet analysis of complex nonstationary oscillatory signals[C]. 4th International Scientific-Practical Conference Problems of Infocommunications, Science and Technology(PIC S&T), 2017:12-20.
[6] Li B X, Wang L, Dong X R. Application of second derivative pretreatment in quantitative analysis using mid-infrared spectrum[J]. Chinese Journal of Analysis Laboratory, 2008, 27(7):9-12.李本祥,王玲,董新荣.二阶导数预处理法在中红外光谱定量分析中的应用研究[J].分析试验室,2008,27(7):9-12.
[7] Yang X K, Zhong M L, Jing X J, et al. FTIR microscopic image analysis based on principal component analysis-2~(nd)derivative spectral imaging[J]. Acta Optica Sinica, 2012, 32(7):0711001.杨秀坤,钟明亮,景晓军,等.基于主成分分析-二阶导数光谱成像的红外显微图像分析[J].光学学报,2012, 32(7):0711001.
[8] Zhang D, Tashpolat·Tiyip, Zhang F, et al.Application of fractional differential in preprocessing hyperspectral data of saline soil[J]. Transactions of the Chinese Society of Agricultural Engineering,2014, 30(24):151-160.张东,塔西甫拉提·特依拜,张飞,等.分数阶微分在盐渍土高光谱数据预处理中的应用[J].农业工程学报,2014, 30(24):151-160.
[9] Cai J H, Xiao Y L, Li X Q. De-noising of near infrared spectra based on generalized S transform and singular value decomposition[J]. Acta Optica Sinica,2018, 38(4):0430005.蔡剑华,肖永良,黎小琴.基于广义S变换和奇异值分解的近红外光谱去噪[J].光学学报,2018, 38(4):0430005.
[10] Xu P, Xiao C, Zhang J C, et al. Denoising method for plant hyperspectral data based on grouped 3D discrete cosine transform dictionary[J]. Acta Optica Sinica, 2017, 37(6):0630003.徐平,肖冲,张竞成,等.基于分组三维离散余弦变换字典的植物高光谱数据去噪方法[J].光学学报,2017, 37(6):0630003.
[11] Coello C A, Lechuga M S. MOPSO:a proposal for multiple objective particle swarm optimization[C].Proceedings of the 2002 Congress on Evolutionary Computation, 2002:1051-1056.
[12] 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.
[13] 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.