Wavelength Selection Algorithm Based on Minimum Correlation Coefficient for Multivariate Calibration

Jie-hong CHENG; Zheng-guang CHEN; Shu-juan YI

doi:10.3964/j.issn.1000-0593(2022)03-0719-07

Journals >Spectroscopy and Spectral Analysis >Volume 42 >Issue 3 >Page 719 > Article

Spectroscopy and Spectral Analysis
Vol. 42, Issue 3, 719 (2022)

Wavelength Selection Algorithm Based on Minimum Correlation Coefficient for Multivariate Calibration

Jie-hong CHENG^1,*, Zheng-guang CHEN^{1,1; 2; *;}, and Shu-juan YI^2,2;

Author Affiliations

¹1. College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China

²2. Heilongjiang Engineering Technology Research Center for Rice Ecological Seedlings Device and Whole Process Mechanization, Daqing 163319, China

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DOI: 10.3964/j.issn.1000-0593(2022)03-0719-07 Cite this Article

Jie-hong CHENG, Zheng-guang CHEN, Shu-juan YI. Wavelength Selection Algorithm Based on Minimum Correlation Coefficient for Multivariate Calibration[J]. Spectroscopy and Spectral Analysis, 2022, 42(3): 719 Copy Citation Text

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Fig. 1. Average value and standard deviation of correlation coefficient

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Fig. 2. Graphic analysis of threshold in grid T

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Fig. 3. Original (a) and Preprocessed (b) NIR spectra of diesel fuels sample

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Fig. 4. Wavelength selection results by MCC

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Fig. 5. Mean and standard deviation of correlation coefficient between selected wavelengths

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Fig. 6. Changes in RMSEV value of MCC-MLR model under different threshold coefficients

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相关系数绝对值	相关强度
0.8~1.0	极强相关
0.6~0.8	强相关
0.4~0.6	中等程度相关
0.2~0.4	弱相关
0.0~0.2	极弱相关或无相关

Table 1. The strength of correlation corresponding to the correlation coefficient between variables

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性质	样本集	样本数	最小值	最大值	平均值	标准差	变异系数/%
Dataset1(BP50)	Whole set	246	197	293	258.38	17.06	6.6
	Calibration set	148	197	293	256.32	19.19	7.5
	Validation set	49	201	280	260.86	12.83	4.9
	Prediction set	49	198	283	262.12	12.62	4.8
Dataset2(SOM)	Whole set	108	42.91	95.85	85.43	10.82	12.7
	Calibration set	65	42.91	95.85	82.52	12.82	15.5
	Validation set	22	76.74	93.24	88.30	4.79	5.4
	Prediction set	21	86.65	93.46	91.40	1.63	1.8

Table 2. Results of sample chemical property

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No.	建模方法	波长数	Validation set		Prediction set
No.	建模方法	波长数	R²	RMSE	R²	RMSE
1	FULL-PLSR	401	0.865 0	4.677 8	0.905 2	3.946 8
2	SPA-MLR	25	0.953 6	2.834 5	0.953 9	2.844 9
3	CARS-PLSR	18	0.928 9	4.760 1	0.927 9	3.544 5
4	IRIV-PLSR	60	0.966 4	3.274 7	0.956 8	2.639 6
5	RF-PLSR	34	0.948 0	4.071 9	0.943 4	3.009 9
6	MCC-MLR	20	0.953 9	2.760 1	0.956 0	2.779 2

Table 3. Model results based on different wavelength selection methods for diesel fuels datasets

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No.	建模方法	波长数	Validation set		Prediction set
No.	建模方法	波长数	R²	RMSE	R²	RMSE
1	FULL-PLSR	850	0.585 2	3.229 9	0.656 2	2.917 6
2	SPA-MLR	23	0.953 9	0.908 9	0.810 1	1.870 9
3	CARS-PLSR	9	0.975 5	1.803 9	0.908 8	1.368 2
4	IRIV-PLSR	33	0.912 4	1.182 5	0.910 6	1.027 9
5	RF-PLSR	25	0.898 8	1.282 7	0.807 4	2.108 4
6	MCC-MLR	12	0.936 1	1.144 7	0.926 5	1.032 3

Table 4. Model results based on different wavelength selection methods for soil datasets

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