Grey Wolf Optimizer for Variable Selection in Quantification of Quaternary Edible Blend Oil by Ultraviolet-Visible Spectroscopy

Abstract

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 (R2) 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.

Application of portable near‐infrared spectroscopy technology for grade identification of Panax notoginseng slices

“…In this study, the grey wolf optimizer (GWO) was used to optimize the LSSVM model (R. Zhang, Wu, et al, 2022). The grey wolf optimization algorithm is a swarm intelligence algorithm that simulates actual wolf predation behaviors with high speed and precision.…”

“…An electric blast drying oven (101-2ab, Wujiang Yabang Electric Heating Technology Co., Ltd., China) was used to dry the fresh PNS. The operating voltage was 220 V, the power of the whole machine was 2.4 kW, the operating temperature range was set to room temperature (+10-250 C), and the temperature control accuracy was ±1 C. In this study, the grey wolf optimizer (GWO) was used to optimize the LSSVM model (R. Zhang, Wu, et al, 2022). The grey wolf optimization algorithm is a swarm intelligence algorithm that simulates actual wolf predation behaviors with high speed and precision.…”

A hybrid variable selection method combining Fisher's linear discriminant combined population analysis and an improved binary cuckoo search algorithm

“…These methods construct multiple models by using different subsets of variables and select the best combination of variables by comparing the performance of the models. In addition, variable selection methods based on swarm intelligence optimization algorithms, such as the gray wolf optimization (GWO) algorithm, 21 whale optimization algorithm (WOA), 22 and buttery optimization algorithm (BOA), 23 have likewise received wide attention. These methods select the best combination of variables by continuously and iteratively adjusting the combination of variable subsets to maximize or minimize the objective function (e.g., classication accuracy or regression prediction error).…”

Discretized butterfly optimization algorithm for variable selection in the rapid determination of cholesterol by near-infrared spectroscopy

“…Variable selection can improve the accuracy of the model by selecting the relevant variables to the target component. [25][26][27] Hence, variable selection is an important step before multivariate calibration.…”