Risk Assessment Model for Pollutants in Edible Oils Based on Optimized Grey Relational Analysis Combined with Extreme Learning Machine

Abstract

In recent years, edible oil safety problems have occurred frequently. In order to reduce the threat of such incidents, it is of great significance to research edible oil safety risk assessment models. Considering that high-dimensional, non-linear and discrete data containing noise are currently obtained from the detection of edible oils, and the existing risk assessment models have several problems such as poor noise suppression, inaccurate evaluation, and strong subjectivity in model parameter adjustment, a risk assessment model for pollutants in edible oils was proposed in this paper. First, risk indicators were selected and data were preprocessed and input into a filtering module based on the wavelet threshold method for filtering. Second, grey relational analysis (GRA) was used to calculate the weight of each risk index and develop a multi-index comprehensive risk label. Extreme learning machine (ELM) was adopted to predict the comprehensive risk value. Third, the practical Bayesian optimization (PBO) algorithm was used to optimize the parameters of filtering module and ELM network. Finally, the fuzzy comprehensive analysis was applied to classify the risk grade of the predicted comprehensive risk value. The application of the proposed model to 150 groups of edible oil data was described in detail. The coefficient of determination (R2) and root mean square error (RMSE) of this model were 0.056 3 and 0.946 1, respectively, indicating its superiority and effectiveness. This study provides reasonable evidence for relevant departments to formulate risk control and sample inspection strategies and optimize the supply chain of edible oils.

Keywords

• edible oil safety; risk assessment; grey relational analysis; extreme learning machine; practical bayesian optimization