基于优化的灰色关联分析-极限学习机食用油污染物风险评价模型研究  

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

作　　者：于家斌[1,2] 范依云 王小艺[1,3] 赵峙尧 金学波 白玉廷[1,2] 王立[1,2] 陈慧敏[1] YU Jiabin;FAN Yiyun;WANG Xiaoyi;ZHAO Zhiyao;JIN Xuebo;BAI Yuting;WANG Li;CHEN Huimin(School of Artificial Intelligence,Beijing Technology and Business University,Beijing 100048,China;Key Laboratory of Industry Industrial Internet and Big Data,China National Light Industry,Beijing Technology and Business University,Beijing 100048,China;School of Arts and Sciences,Beijing Institute of Fashion Technology,Beijing 100029,China)

机构地区：[1]北京工商大学人工智能学院,北京100048 [2]北京工商大学中国轻工业工业互联网与大数据重点实验室,北京100048 [3]北京服装学院文理学院,北京100029

出　　处：《食品科学》2023年第3期88-97,共10页Food Science

基　　金：“十三五”国家重点研发计划重点专项(2020YFC1606801);北京市自然科学基金项目(4222042)。

摘　　要：近年来食用油安全事故频发,为降低这类事件的威胁,对其风险评价模型进行研究有着极其重要的意义。针对目前食用油检测数据高维性、非线性、离散性和含噪声的特点,现有风险评价模型存在噪声抑制能力差、评价不准确和模型参数调整主观性强等问题。对此,本实验提出一种食用油污染物风险评价模型。首先进行风险指标筛选以及数据预处理,然后将处理后的数据输入到基于小波阈值法的滤波模块中进行滤波,随后通过灰色关联分析计算各风险指标的权重来制定多指标综合风险值标签;由极限学习机(extreme learning machine,ELM)对综合风险值进行预测,在上述过程中利用实用贝叶斯优化算法分别来优化滤波模块和ELM网络的参数;最后利用模糊综合分析对预测综合风险值进行风险等级划分。本研究依托150组食用油数据进行分析,详细阐述了该模型的使用流程,通过不同模型对比实验,本研究模型决定系数R2和均方根误差分别为0.0563和0.9461,进一步验证了方法的优越性和有效性,可以为相关部门制定风险控制策略、抽检策略以及优化加工链提供更为合理的依据。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(R~2)and root mean square error(RMSE)of this model were 0.0563 and 0.9461,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.

关 键 词：食用油安全 风险评价 灰色关联分析 极限学习机 实用贝叶斯优化 

分 类 号：TS210.1[轻工技术与工程—粮食、油脂及植物蛋白工程] TP183[轻工技术与工程—食品科学与工程]