Rapid spectral analysis of agro-products using an optimal strategy: dynamic backward interval PLS–competitive adaptive reweighted sampling

A novel strategy of variable selection approach named dynamic backward interval partial least squares–competitive adaptive reweighted sampling (DBiPLS-CARS) was proposed in this study. Near-infrared data sets of three different agro-products, namely corn, crop processing lamina, and plant leaf sampl...

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Veröffentlicht in:	Analytical and bioanalytical chemistry Jg. 412; H. 12; S. 2795 - 2804
Hauptverfasser:	Song, Xiangzhong, Du, Guorong, Li, Qianqian, Tang, Guo, Huang, Yue
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2020 Springer Springer Nature B.V
Schlagworte:	Adaptive sampling Algorithms Analytical Chemistry Biochemistry Business competition Cereal crops Characterization and Evaluation of Materials Chemistry Chemistry and Materials Science corn crop residues Crops, Agricultural - chemistry data collection Food Science Laboratory Medicine leaves Monitoring/Environmental Analysis Monte Carlo Method Plant Leaves - chemistry prediction processing residues rapid methods Regression analysis Research Paper Sampling Spectral analysis Spectroscopy, Near-Infrared - methods Spectrum analysis Zea mays - chemistry China Monte Carlo uninformative variable elimination (MCUVE) Dynamic backward interval partial least squares (DBiPLS) Competitive adaptive reweighted sampling (CARS) Variable selection Agro-products
ISSN:	1618-2642, 1618-2650, 1618-2650
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Abstract	A novel strategy of variable selection approach named dynamic backward interval partial least squares–competitive adaptive reweighted sampling (DBiPLS-CARS) was proposed in this study. Near-infrared data sets of three different agro-products, namely corn, crop processing lamina, and plant leaf samples, were collected to investigate the performance of the proposed method. Weak relevant variables were first removed by DBiPLS and a refined selection of the remaining variables was then conducted by CARS. The Monte Carlo uninformative variable elimination (MCUVE) was used as a classical beforehand uninformative variable elimination method for comparison. Results showed that DBiPLS can select informative variables more continuously than MCUVE. Some synergistic variables which may be omitted by MCUVE can be retained by DBiPLS. By contrast, MCUVE can hardly avoid the disturbance of certain weak relevant variables as a result of its calculation based on the full spectrum regression. Therefore, DBiPLS exhibited the advantage of removing the weak relevant variables before CARS, and simultaneously improved the prediction performance of CARS.
AbstractList	A novel strategy of variable selection approach named dynamic backward interval partial least squares-competitive adaptive reweighted sampling (DBiPLS-CARS) was proposed in this study. Near-infrared data sets of three different agro-products, namely corn, crop processing lamina, and plant leaf samples, were collected to investigate the performance of the proposed method. Weak relevant variables were first removed by DBiPLS and a refined selection of the remaining variables was then conducted by CARS. The Monte Carlo uninformative variable elimination (MCUVE) was used as a classical beforehand uninformative variable elimination method for comparison. Results showed that DBiPLS can select informative variables more continuously than MCUVE. Some synergistic variables which may be omitted by MCUVE can be retained by DBiPLS. By contrast, MCUVE can hardly avoid the disturbance of certain weak relevant variables as a result of its calculation based on the full spectrum regression. Therefore, DBiPLS exhibited the advantage of removing the weak relevant variables before CARS, and simultaneously improved the prediction performance of CARS. A novel strategy of variable selection approach named dynamic backward interval partial least squares-competitive adaptive reweighted sampling (DBiPLS-CARS) was proposed in this study. Near-infrared data sets of three different agro-products, namely corn, crop processing lamina, and plant leaf samples, were collected to investigate the performance of the proposed method. Weak relevant variables were first removed by DBiPLS and a refined selection of the remaining variables was then conducted by CARS. The Monte Carlo uninformative variable elimination (MCUVE) was used as a classical beforehand uninformative variable elimination method for comparison. Results showed that DBiPLS can select informative variables more continuously than MCUVE. Some synergistic variables which may be omitted by MCUVE can be retained by DBiPLS. By contrast, MCUVE can hardly avoid the disturbance of certain weak relevant variables as a result of its calculation based on the full spectrum regression. Therefore, DBiPLS exhibited the advantage of removing the weak relevant variables before CARS, and simultaneously improved the prediction performance of CARS.A novel strategy of variable selection approach named dynamic backward interval partial least squares-competitive adaptive reweighted sampling (DBiPLS-CARS) was proposed in this study. Near-infrared data sets of three different agro-products, namely corn, crop processing lamina, and plant leaf samples, were collected to investigate the performance of the proposed method. Weak relevant variables were first removed by DBiPLS and a refined selection of the remaining variables was then conducted by CARS. The Monte Carlo uninformative variable elimination (MCUVE) was used as a classical beforehand uninformative variable elimination method for comparison. Results showed that DBiPLS can select informative variables more continuously than MCUVE. Some synergistic variables which may be omitted by MCUVE can be retained by DBiPLS. By contrast, MCUVE can hardly avoid the disturbance of certain weak relevant variables as a result of its calculation based on the full spectrum regression. Therefore, DBiPLS exhibited the advantage of removing the weak relevant variables before CARS, and simultaneously improved the prediction performance of CARS.
Audience	Academic
Author	Du, Guorong Li, Qianqian Tang, Guo Huang, Yue Song, Xiangzhong
Author_xml	– sequence: 1 givenname: Xiangzhong surname: Song fullname: Song, Xiangzhong organization: College of Food Science and Nutritional Engineering, China Agricultural University – sequence: 2 givenname: Guorong surname: Du fullname: Du, Guorong organization: Beijing Third Supervision Station of Tobacco – sequence: 3 givenname: Qianqian surname: Li fullname: Li, Qianqian organization: School of Marine Science, China University of Geosciences – sequence: 4 givenname: Guo surname: Tang fullname: Tang, Guo organization: College of Food Science and Nutritional Engineering, China Agricultural University – sequence: 5 givenname: Yue orcidid: 0000-0001-8563-232X surname: Huang fullname: Huang, Yue email: huangyue@cau.edu.cn organization: College of Food Science and Nutritional Engineering, China Agricultural University
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Copyright	Springer-Verlag GmbH Germany, part of Springer Nature 2020 COPYRIGHT 2020 Springer Springer-Verlag GmbH Germany, part of Springer Nature 2020.
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Keywords	Monte Carlo uninformative variable elimination (MCUVE) Dynamic backward interval partial least squares (DBiPLS) Competitive adaptive reweighted sampling (CARS) Variable selection Agro-products
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Snippet	A novel strategy of variable selection approach named dynamic backward interval partial least squares–competitive adaptive reweighted sampling (DBiPLS-CARS)... A novel strategy of variable selection approach named dynamic backward interval partial least squares-competitive adaptive reweighted sampling (DBiPLS-CARS)...
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SubjectTerms	Adaptive sampling Algorithms Analytical Chemistry Biochemistry Business competition Cereal crops Characterization and Evaluation of Materials Chemistry Chemistry and Materials Science corn crop residues Crops, Agricultural - chemistry data collection Food Science Laboratory Medicine leaves Monitoring/Environmental Analysis Monte Carlo Method Plant Leaves - chemistry prediction processing residues rapid methods Regression analysis Research Paper Sampling Spectral analysis Spectroscopy, Near-Infrared - methods Spectrum analysis Zea mays - chemistry
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Title	Rapid spectral analysis of agro-products using an optimal strategy: dynamic backward interval PLS–competitive adaptive reweighted sampling
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