食品科学 ›› 2021, Vol. 42 ›› Issue (1): 197-207.doi: 10.7506/spkx1002-6630-20191210-104

• 营养卫生 • 上一篇    下一篇

集成模糊层级划分的LightGBM食品安全风险预警模型:以肉制品为例

高亚男,王文倩,王建新   

  1. (北京林业大学信息学院,北京 100083)
  • 发布日期:2021-01-18
  • 基金资助:
    “十三五”国家重点研发计划重点专项(2018YFC1603302;2018YFC1603305)

A Food Safety Risk Prewarning Model Using LightGBM Integrated with Fuzzy Hierarchy Partition: A Case Study for Meat Products

GAO Yanan, WANG Wenqian, WANG Jianxin   

  1. (School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China)
  • Published:2021-01-18

摘要: 在食品安全风险管理中,风险点精确定位能从源头解决食品安全问题,对食品安全风险评估和预警具有关键意义。近年来,食品行业信息化的发展使得原料生产、加工、仓储运输、抽检等环节积累了大量数据,并亟待开发利用。而现存的风险预警方法存在风险细粒度难以衡量、数据利用率低、人工成本高等问题。因此,本研究首先对食品安全相关数据进行归纳分类并描述数据特征,作为后续处理的基础。为了充分利用食品安全数据海量、高维的特点,本研究使用先验风险概率与模糊层级划分相结合的方法对不同类型的属性计算模糊综合风险值,作为预测模型标签值。由LightGBM和专家干预策略结合产生的预测模型可对风险值进行预测和校正。基于此,依托肉制品和水产品数据的实验详细阐述了方法的使用流程,并进一步验证了方法优越性和规律合理性。研究中产出的风险分析结果,包括风险值和属性重要程度分布等可以为决策者提供有价值的决策依据。

关键词: 食品安全;风险预警;模糊层级划分;梯度提升树;LightGBM

Abstract: In food safety risk management, accurately locating risk points is of critical significance to food safety risk assessment and prewarning, since it helps solve food safety problems from the source. Recently, with the development of informationization in the food industry, a large amount of food safety data accumulated during raw material production, food processing, storage and transportation and, sample inspection urgently need to be developed and utilized. However, there exist lots of deficiencies in the existing food safety risk prewarning system, such as rough risk measurement, low data utilization rate, and high labor cost. Therefore, in this paper, we sorted the food safety data and described the data features for subsequent processing. Meanwhile, in order to take full advantages of the data features of large quantity and high dimension, the combination of prior risk probability and fuzzy hierarchy partition was employed to calculate fuzzy comprehensive risk values based on various attributes for use as the expected output of a predictive model that can predict and validate risk values, generated using light gradient boosting machine (LightGBM) combined with experts’ modification operations. Finally, data on meat products and aquatic products were used to illustrate how to use this method, and its superiority and reasonability were validated. The risk analysis results in this paper, including the risk values and attribute importance distribution, can provide decision makers with valuable information.

Key words: food safety; risk prewarning; fuzzy hierarchy partition; gradient boosting decision tree; light gradient boosting machine

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