集成改进AHP与XGBoost算法的食品安全风险预测模型:以大米为例

近年来,我国在食品质量安全管控方面已有较大提升,但伴随着食品产业规模的增大,检验需求量的增多,食品安全检测数据出现高维、复杂且非线性等特征,这些特征会导致定量分析数据利用率低,从而直接影响以数据为载体的风险预测模型的准确性。为提高风险预测模型的准确性,以食品安全检测数据为基础,提出了一种集成层次分析法与极端梯度提升树算法的食品安全风险预测模型,并通过食品安全限定指标对集成模型进行优化改进,从而实现更高效准确的食品安全风险评估。研究以除港澳台外的全国31个省大米危害物检测数据为例,详细阐述了模型的使用方法,检验结果表明,该风险预测模型具有较强的平稳性与较高的准确性。研究旨在为食品安全监管部门评估决策提供一定的理论依据及参考。

A Food Safety Risk Forecast Model Integrated With Improved AHP and XGBoost Algorithm:A Case Study of Rice

In recent years, China has made great improvements in food quality and safety control, but with the increase in the scale of the food industry, the demand for inspections has increased. Moreover, food safety inspection data has appeared high-dimensional, complex and non-linear characteristics, and these features will lead to low utilization of quantitative analysis data, which directly affects the accuracy of the risk forecast model based on data. This study proposed a risk forecast model of food safety which integrated analytic hierarchy process and extreme gradient boosting tree algorithm based on food safety inspection data, and the integrated model was optimized and improved by food safety restricted indicators, so as to achieve more efficient and accurate food safety risk assessment. Based on this, the rice hazard detection data of 31 provinces across the country except Hong Kong, Macao and Taiwan were used as examples to elaborate on the use of the model. The result of the model test revealed that the risk forecast model had strong stability and high accuracy, which could provide certain theoretical basis and reference for the evaluation and decision-making of food safety regulatory authorities.