基于风险熵的农产品安全定量评价研究

doi:10.19788/j.issn.2096-6369.200406

摘要/Abstract

摘要： 目的

由于监测样品的多数检测值被记录为“未检出”，农产品安全监测数据呈现出高度的稀疏性。为获得足够的决策参考信息，农产品安全评价研究者往往需要将多个监测项目的异构稀疏型监测数据进行有效融合，并对风险进行准确定量。

方法

本文围绕农产品安全风险管理需求，提出了一种基于产品分类与限量、以计算“产品+指标”组合的风险熵为目标的数据融合策略，并应用综合评价理论与方法，开展了基于风险熵的农产品安全性宏观定量评价研究。

结果

蔬菜中农药残留安全性实例分析结果表明：风险熵能够有效提取风险监测融合数据所包含的风险信息并对其进行有效定量；基于风险熵的安全性评价可以获得完整的风险分布信息，并给出阈值划分与风险排序结果；风险熵对潜在风险的识别更为准确，可以有效避免定性评价对风险的过高或过低估计。

结论

上述研究工作表明，围绕风险熵的数据融合与定量评价在技术上是可行的，基于风险熵的定量评价改进了定性评价在风险识别与风险大小描述等方面的不足，能够为农产品安全风险管理工作提供更加准确和丰富的参考信息。

关键词: 数据融合, 风险熵, 定量评价, 农产品安全, 安全风险管理, 食品安全, 粮食安全

Abstract: Objectives

Safety monitoring data for agricultural products are currently limited as most tested indicators of the monitored samples are recorded as “undetected”, presenting a major research obstacle as researchers must still attempt to extract sufficient information. To acquire reference information for decision making and accurately quantify risks, researchers of agricultural product safety evaluation protocols need effective methods to combine the heterogeneous and sparse monitoring data from multiple monitoring projects.

Methods

Risk management is crucial to the safety of agricultural products. Focusing on the requirements of safety risk management of agricultural products, a product category and limited quantity-based strategy of data fusion was proposed to calculate the risk entropy of the “product + index” combination. Subsequently, using the theory and method of comprehensive evaluation, a macroscopic quantitative evaluation of agricultural products based on risk entropy was conducted.

Results

According to a case analysis of the safety of pesticide residues in vegetables, risk entropy can effectively extract risk information in the fused data of risk monitoring and be used in quantitative analysis. Moreover, the risk-entropy-based safety evaluation can provide complete information about risk distribution, as well as the division of risk threshold and risk ranking. It was further found that the risk entropy can accurately identify potential risk, thus effectively avoiding overestimated or underestimated risks in qualitative evaluation.

Conclusions

The findings of this study indicate that the proposed risk-entropy-based data fusion and quantitative evaluation are technically feasible. This quantitative evaluation method has been shown to improve the performance of qualitative evaluation in risk identification and description. In conclusion, the method increases the abundance of accurate references for safety risk management of agricultural products, thus promoting the progress of agricultural product safety risk management.

Key words: data fusion, risk entropy, quantitative evaluation, agricultural product safety, risk management, food safety, food security

中图分类号:

F322

陈志军, 刘艳, 钱永忠. 基于风险熵的农产品安全定量评价研究[J]. 农业大数据学报, 2020, 2(4): 47-54.

Zhijun Chen, Yan Liu, Yongzhong Qian. Quantitative Evaluation of Agricultural Product Safety Based on Risk Entropy[J]. Journal of Agricultural Big Data, 2020, 2(4): 47-54.

图/表 6

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参考文献 17

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数据项	样本数（个）	品种/类别（个）	农药残留（个）
监测数据A	44,466	52	64
监测数据B	32,491	47	58
融合数据	76,957	42	68

农产品	L_i	排序	农产品	L_i	排序
芹菜 celery	0.1008	1	甜椒 sweet pepper	0.0288	22
长豇豆 asparagus bean	0.0927	2	茄子 eggplant	0.0283	23
韭菜 chinese chives	0.0843	3	苦瓜 balsam pear	0.0266	24
其他 others	0.0796	4	番茄 tomato	0.0262	25
叶用莴苣 leaf lettuce	0.0685	5	黄瓜 cucumber	0.0213	26
普通白菜 pak-choi	0.0656	6	杏鲍菇 king oyster mushroom	0.0209	27
蕹菜 water spinach	0.0643	7	花椰菜 cauliflower	0.0179	28
菠菜 spinach	0.0636	8	丝瓜 luffa	0.0164	29
大葱 welsh onion	0.0541	9	山药 chinese yam	0.0151	30
大白菜 chinese cabbage	0.0488	10	香菇 shiitake	0.015	31
细香葱 chive	0.0475	11	西葫芦 marrow	0.0146	32
辣椒 pepper	0.0452	12	结球甘蓝 cabbage	0.0136	33
茼蒿 garland chrysanthemum	0.0362	13	萝卜 radish	0.0136	34
茎用莴苣 asparagus lettuce	0.0357	14	双孢蘑菇 white mushroom	0.0135	35
平菇 oyster mushroom	0.0354	15	胡萝卜 carrot	0.0116	36
菜薹(心) flowering chinese cabbage	0.0354	16	马铃薯 potato	0.0097	37
菜豆 kidney bean	0.0346	17	洋葱 onion	0.0088	38
青花菜 broccoli	0.0334	18	秀珍菇 pleurotus geesteranus	0.0087	39
大蒜 garlic	0.0309	19	黑木耳 black fungus	0.008	40
姜 ginger	0.0305	20	金针菇 golden needle mushroom	0.0037	41
茶树菇 agrocybe cylindracea	0.0301	21	南瓜 pumpkin	0.0027	42

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