A Spectrophotometric Detecting Method of Trace Copper Ion in Zinc Solution Based on Partition Modeling

Fig. 3. (a) Spectral signals of different mass concentrations of Cu(II) in the background of high mass concentration of Zn(II)and trace concentration of Co(II); (b) relationship between Cu(II) mass concentration and spectral signal at 9 wavelength points

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Fig. 4. Correlation coefficient-stability value of wavelength variables of trace Cu(II)

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Fig. 5. Stability value of wavelength variables with MC-UVE PLS method

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Fig. 6. CVmse of models with different number of wavelength variables

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Fig. 7. Scatter plots of predicted and measured mass concentrations of Cu(II)

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Table 1. Fitting functions of Cu(II) mass concentration c and spectral signal at different wavelengths

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Table 1. Fitting functions of Cu(II) mass concentration c and spectral signal at different wavelengths

Wavelength /nm	Fitting method	Fitting function
	Linear fitting	2.8×10^-3c+3.8×10^-3
500	Nonlinear fitting	-2.2×10^-5c³+6.1×10^-5c²+2.9×10^-3c+3.6×10^-3
	Partition fitting	1) -6.1×10^-4c³+1.8×10^-3c²+1.4×10^-3c+3.9×10^-3;2) 2.5×10^-3c+4.5×10^-3
	Linear fitting	9.1×10^-4c+2.4×10^-3
520	Nonlinear fitting	-1.8×10^-6c³-6.0×10^-5c²+1.2×10^-3c+2.2×10^-3
	Partition fitting	1) -5.4×10^-4c³+1.5×10^-3c²-1.3×10^-4c+2.4×10^-3;2) 7.1×10^-4c+3.0×10^-3
	Linear fitting	3.8×10^-4c+2.6×10^-3
540	Nonlinear fitting	-1.3×10^-6c³-5.4×10^-5c²+6.3×10^-4c+2.4×10^-3
	Partition fitting	1) -4.5×10^-4c³+1.2×10^-3c²-3.2×10^-4c+2.6×10^-3;2) 2.2×10^-4c+3.1×10^-3

Table 2. Parameters of SVM model
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Table 2. Parameters of SVM model
Ion SVM model Penalty parameter C Kernel function parameter σ
PSO-SVC 4.1095 0.10
Cu(II) PSO-SVR with low concentration interval 8.8250 0.01
PSO-SVR with high concentration interval 4.1604 0.01

Table 3. Confusion matrix of mass concentration interval prediction results for Cu(II)
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Table 3. Confusion matrix of mass concentration interval prediction results for Cu(II)
Item Predicted interval of mass concentration
High Low
True interval of High 5 0
mass concentration Low 0 9

Table 4. Modeling results based on seven methods

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Table 4. Modeling results based on seven methods

Ion	Model	Wavelength number	Maximum relative error /%	R² /%	RMSEP
	PLS	401	13.78	98.16	0.1424
	PSO-SVR	401	97.53	93.23	0.2835
	CARS PLS	46	10.88	99.06	0.1020
Cu(II)	MC-UVE PLS	67	8.13	98.99	0.1057
	MC -UVE LS SVM	67	14.79	99.18	0.0986
	VR-S SVR	50	8.32	99.47	0.0794
	VR-S C-SVR	50	6.94	99.61	0.0678

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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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Paper Information

Category: Spectroscopy

Received: Aug. 8, 2018

Accepted: Sep. 29, 2018

Published Online: May. 10, 2019

The Author Email:

DOI:10.3788/AOS201939.0230002

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Fitting functions of Cu(II) mass concentration c and spectral signal at different wavelengths

Table 1. Fitting functions of Cu(II) mass concentration c and spectral signal at different wavelengths

Table 2. Parameters of SVM model

Table 2. Parameters of SVM model

Table 3. Confusion matrix of mass concentration interval prediction results for Cu(II)

Table 3. Confusion matrix of mass concentration interval prediction results for Cu(II)

Table 4. Modeling results based on seven methods

Table 4. Modeling results based on seven methods