A Rice Security Risk Assessment Method Based on the Fusion of Multiple Machine Learning Models

Abstract

With the accelerated digital transformation, food security data is exponentially growing, making it difficult to process and analyze data as the primary challenge for food security risk regulation. The promotion of “big data + food” safety supervision can effectively reduce supervision costs and improve the efficiency of risk detection and response. In order to improve the utilization of testing data and achieve rapid risk assessment, this paper proposes a rice security risk assessment method based on the fusion of multiple machine learning models, and conducts experimental validation based on rice hazard detection data from 31 provinces in China excluding Hong Kong, Macao and Taiwan in 2018. The model comparison verifies that the risk assessment model shows better performance than other mainstream machine learning algorithms, and its evaluation accuracy is as high as 99.54%, which verifies that the model proposed in this paper is more stable and accurate, and can provide accurate and efficient decision-making basis for regulatory authorities.

Advancing food safety risk assessment in China: development of new approach methodologies (NAMs)

“…Wang et al (2022) used the voting-ensemble deep learning method and the submodels to analyze the risks of heavy metals in grain products. Xu et al (2022b) combined three machine learning models to analyze multiple hazards (heavy metal, mycotoxin, pollutants) in rice. Other AI models used include agglomerative hierarchical clustering-radial basis function neural network integrating an analytic hierarchy process approach and the entropy weight, analytic hierarchy process integrated extreme learning machine based approach, and analytic hierarchy process based on the entropy weight and the autoencoder-recurrent neural network integrated framework for early warning analyses (Geng et al, 2017;Geng et al, 2021;Zhong et al, 2023).…”