灰狼优化算法在紫外可见光谱法测定四元食用混合油含量中的变量选择。
Grey Wolf Optimizer for Variable Selection in Quantification of Quaternary Edible Blend Oil by Ultraviolet-Visible Spectroscopy.
机构信息
State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China.
Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin 644000, China.
出版信息
Molecules. 2022 Aug 12;27(16):5141. doi: 10.3390/molecules27165141.
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) 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.
一种新颖的群体智能算法——离散灰狼优化算法（GWO）被首次引入到食用混合油分析中的变量选择工具中。在该方法中，通过逻辑函数对狼的位置进行更新和离散化。研究了狼群的性能、迭代次数和狼的数量。使用偏最小二乘法（PLS）建立和预测样品中单一油的含量。为了验证该方法，使用紫外可见分光光度计（UV-Vis）对包含大豆油、葵花籽油、花生油和芝麻油的 102 个食用混合油样品进行了测量。结果表明，与全光谱 PLS、蒙特卡罗无信息变量消除 PLS（MCUVE-PLS）和随机化测试 PLS（RT-PLS）相比，GWO-PLS 模型可以用最少的变量提供最佳的预测精度。GWO-PLS 的确定系数（R）均高于 0.95。因此，该研究表明了离散 GWO 在快速测定四元食用混合油中的可行性。
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