Spectral Selection Method Based on Ant Colony-Genetic Algorithm

Fig. 1. Original spectra of samples of six different brands
(a): Yilizhennong; (b): Yiliskim milk; (c): Mengniu high calcium milk; (d): QQ star; (e): The land; (f): Telunsu

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Fig. 2. PLS-ACO-GA algorithm flowchart

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Fig. 3. PLS-ACO band contributionrate

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Fig. 4. PLS-ACO-GA band contributionrate

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Fig. 5. MSC-(PLS-ACO-GA)-RF prediction results

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Table 1. The prediction results of the PLS regression model for the milk fat content of different pretreatment methods

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Table 1. The prediction results of the PLS regression model for the milk fat content of different pretreatment methods

预处理方法	$R_{c}^{2}$	$R_{p}^{2}$	RMSECV	RMSEP
Raw data	0.999 1	0.987 3	0.046 6	0.143 7
1^st Der	0.998 3	0.973 6	0.064 7	0.207 4
2^st Der	0.999 1	0.954 6	0.046 7	0.270 0
S-G	0.998 8	0.988 8	0.053 4	0.135 1
MSC	0.999 9	0.993 4	0.015 5	0.103 8
SNV	0.999 0	0.988 0	0.049 4	0.139 6

Table 2. PLS regression model results with PLS-ACO and max-features set to 10, 20 and 30 respectively
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Table 2. PLS regression model results with PLS-ACO and max-features set to 10, 20 and 30 respectively
max-features 波长数 $R_{p}^{2}$ RMSEP 主成分数
Full 125 0.993 4 0.103 8 24
10 21 0.996 2 0.077 8 10
20 18 0.997 0 0.069 2 16
30 41 0.997 8 0.058 5 12

Table 3. PLS regression model results with PLS-ACO-GA and max-features set to 10, 20 and 30 respectively
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Table 3. PLS regression model results with PLS-ACO-GA and max-features set to 10, 20 and 30 respectively
max-features 波长数 $R_{p}^{2}$ RMSEP 主成分数
Full 125 0.934 4 0.103 8 24
10 16 0.996 8 0.075 2 8
20 16 0.997 6 0.062 2 14
30 32 0.998 0 0.056 5 12

Table 4. PLS regression model result sunder different feature selection methods
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Table 4. PLS regression model result sunder different feature selection methods
方法波长数 $R_{p}^{2}$ RMSEP 主成分数
Full 125 0.934 4 0.103 8 24
CARS 40 0.995 1 0.088 7 12
GA 43 0.997 1 0.068 5 14
ACO 22 0.996 2 0.072 8 12
PLS-ACO 18 0.997 0 0.077 8 16
PLS-ACO-GA 16 0.997 6 0.062 2 14

Table 5. PLS-ACO and PLS-ACO-GA MLR results
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Table 5. PLS-ACO and PLS-ACO-GA MLR results
方法波长数 $R_{p}^{2}$ RMSEP
Full-MLR 125 0.991 4 0.118 0
PLS-ACO-MLR 18 0.997 0 0.069 2
PLS-ACO-GA-MLR 16 0.997 6 0.062 3

Table 6. PLS-ACO and PLS-ACO-GA RFR results
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Table 6. PLS-ACO and PLS-ACO-GA RFR results
方法波长数 $R_{p}^{2}$ RMSEP
Full-RFR 125 0.999 963 0.007 6
PLS-ACO-RFR 18 0.999 974 0.006 4
PLS-ACO-GA-RFR 16 0.999 994 0.003 0

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Qing HUANG, He-ru XUE, Jiang-ping LIU, Mei-chen LIU, Peng-wei HU, De-gang SUN. Spectral Selection Method Based on Ant Colony-Genetic Algorithm[J]. Spectroscopy and Spectral Analysis, 2022, 42(7): 2262

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Paper Information

Category: Orginal Article

Received: Jul. 29, 2021

Accepted: --

Published Online: Nov. 16, 2022

The Author Email:

DOI:10.3964/j.issn.1000-0593(2022)07-2262-07

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. The prediction results of the PLS regression model for the milk fat content of different pretreatment methods

Table 1. The prediction results of the PLS regression model for the milk fat content of different pretreatment methods

Table 2. PLS regression model results with PLS-ACO and max-features set to 10, 20 and 30 respectively

Table 2. PLS regression model results with PLS-ACO and max-features set to 10, 20 and 30 respectively

Table 3. PLS regression model results with PLS-ACO-GA and max-features set to 10, 20 and 30 respectively

Table 3. PLS regression model results with PLS-ACO-GA and max-features set to 10, 20 and 30 respectively

Table 4. PLS regression model result sunder different feature selection methods

Table 4. PLS regression model result sunder different feature selection methods

Table 5. PLS-ACO and PLS-ACO-GA MLR results

Table 5. PLS-ACO and PLS-ACO-GA MLR results

Table 6. PLS-ACO and PLS-ACO-GA RFR results

Table 6. PLS-ACO and PLS-ACO-GA RFR results