Grey Wolf Optimizer for Variable Selection in Quantification of Quaternary Edible Blend Oil by Ultraviolet-Visible Spectroscopy
A novel swarm intelligence algorithm, discretized grey wolf optimizer (GWO), was introduced as a variable selection tool in edible blend oil analysis for the first time. In the approach, positions of wolves were updated and then discretized by logical function. The performance of a wolf pack, the it...
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| Format: | Article |
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MDPI AG
2022-08-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/27/16/5141 |
| _version_ | 1797408718353072128 |
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| author | Rongling Zhang Xinyan Wu Yujie Chen Yang Xiang Dan Liu Xihui Bian |
| author_facet | Rongling Zhang Xinyan Wu Yujie Chen Yang Xiang Dan Liu Xihui Bian |
| author_sort | Rongling Zhang |
| collection | DOAJ |
| description | A novel swarm intelligence algorithm, discretized grey wolf optimizer (GWO), was introduced as a variable selection tool in edible blend oil analysis for the first time. In the approach, positions of wolves were updated and then discretized by logical function. The performance of a wolf pack, the iteration number and the number of wolves were investigated. The partial least squares (PLS) method was used to establish and predict single oil contents in samples. To validate the method, 102 edible blend oil samples containing soybean oil, sunflower oil, peanut oil and sesame oil were measured by an ultraviolet-visible (UV-Vis) spectrophotometer. The results demonstrated that GWO-PLS models can provide best prediction accuracy with least variables compared with full-spectrum PLS, Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). The determination coefficients (R<sup>2</sup>) of GWO-PLS were all above 0.95. Therefore, the research indicates the feasibility of using discretized GWO for variable selection in rapid determination of quaternary edible blend oil. |
| first_indexed | 2024-03-09T04:02:34Z |
| format | Article |
| id | doaj.art-768cca621b724526a425d46fbc863b0e |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-09T04:02:34Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-768cca621b724526a425d46fbc863b0e2023-12-03T14:11:09ZengMDPI AGMolecules1420-30492022-08-012716514110.3390/molecules27165141Grey Wolf Optimizer for Variable Selection in Quantification of Quaternary Edible Blend Oil by Ultraviolet-Visible SpectroscopyRongling Zhang0Xinyan Wu1Yujie Chen2Yang Xiang3Dan Liu4Xihui Bian5State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, ChinaState Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, ChinaState Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, ChinaState Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, ChinaState Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, ChinaState Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, ChinaA novel swarm intelligence algorithm, discretized grey wolf optimizer (GWO), was introduced as a variable selection tool in edible blend oil analysis for the first time. In the approach, positions of wolves were updated and then discretized by logical function. The performance of a wolf pack, the iteration number and the number of wolves were investigated. The partial least squares (PLS) method was used to establish and predict single oil contents in samples. To validate the method, 102 edible blend oil samples containing soybean oil, sunflower oil, peanut oil and sesame oil were measured by an ultraviolet-visible (UV-Vis) spectrophotometer. The results demonstrated that GWO-PLS models can provide best prediction accuracy with least variables compared with full-spectrum PLS, Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). The determination coefficients (R<sup>2</sup>) of GWO-PLS were all above 0.95. Therefore, the research indicates the feasibility of using discretized GWO for variable selection in rapid determination of quaternary edible blend oil.https://www.mdpi.com/1420-3049/27/16/5141edible blend oilspectral analysisvariable selectionmultivariate calibrationgrey wolf optimizer |
| spellingShingle | Rongling Zhang Xinyan Wu Yujie Chen Yang Xiang Dan Liu Xihui Bian Grey Wolf Optimizer for Variable Selection in Quantification of Quaternary Edible Blend Oil by Ultraviolet-Visible Spectroscopy Molecules edible blend oil spectral analysis variable selection multivariate calibration grey wolf optimizer |
| title | Grey Wolf Optimizer for Variable Selection in Quantification of Quaternary Edible Blend Oil by Ultraviolet-Visible Spectroscopy |
| title_full | Grey Wolf Optimizer for Variable Selection in Quantification of Quaternary Edible Blend Oil by Ultraviolet-Visible Spectroscopy |
| title_fullStr | Grey Wolf Optimizer for Variable Selection in Quantification of Quaternary Edible Blend Oil by Ultraviolet-Visible Spectroscopy |
| title_full_unstemmed | Grey Wolf Optimizer for Variable Selection in Quantification of Quaternary Edible Blend Oil by Ultraviolet-Visible Spectroscopy |
| title_short | Grey Wolf Optimizer for Variable Selection in Quantification of Quaternary Edible Blend Oil by Ultraviolet-Visible Spectroscopy |
| title_sort | grey wolf optimizer for variable selection in quantification of quaternary edible blend oil by ultraviolet visible spectroscopy |
| topic | edible blend oil spectral analysis variable selection multivariate calibration grey wolf optimizer |
| url | https://www.mdpi.com/1420-3049/27/16/5141 |
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