Grey wolf optimizer for variable selection in quantification of quaternary edible blend oil by ultraviolet-visible spectroscopy
作者：Rongling Zhang, Xinyan Wu, Yujie Chen, Yang Xiang, Dan Liu, Xihui Bian*
關(guān)鍵字：Edible blend oil; Spectral analysis; Variable selection; Multivariate calibration; Grey wolf optimizer
論文來(lái)源：期刊
具體來(lái)源：Molecules, 2022, 27 (16), 5141
發(fā)表時(shí)間：2022年
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 wolf pack, the iteration number and the number of wolves were investigated. The partial least squares (PLS) 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 ultraviolet-visible (UV-Vis) spectrophotometer. Results demonstrate 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 (R2) of GWO-PLS are 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.