花生黄曲霉毒素平衡取样-随机森林风险预警模型的应用研究

doi:10.3864/j.issn.0578-1752.2022.17.013

中国农业科学 ›› 2022, Vol. 55 ›› Issue (17): 3426-3436.doi: 10.3864/j.issn.0578-1752.2022.17.013

花生黄曲霉毒素平衡取样-随机森林风险预警模型的应用研究

郭灿^1,⁴(),岳晓凤^1,^3,⁴(),白艺珍^1,^3,^4,⁵,张良晓^1,^2,^3,⁴,张奇^1,^2,^3,^4,⁵(),李培武^1,^2,^3,^4,⁵()

¹中国农业科学院油料作物研究所,武汉 430062
²农业农村部油料作物生物学与遗传育种重点实验室,武汉 430062
³国家农业检测基准实验室（生物毒素）,武汉 430062
⁴农业农村部油料产品质量安全风险评估实验室（武汉）,武汉 430062
⁵农业农村部油料及制品质量监督检验测试中心,武汉 430062

收稿日期:2022-01-14 接受日期:2022-05-17 出版日期:2022-09-01 发布日期:2022-09-07
通讯作者: 张奇,李培武
作者简介:郭灿,E-mail： guocan2020@163.com。|岳晓凤,E-mail： yuexf2017@caas.cn。
基金资助:
国家自然科学基金重点项目(32030085);国家农产品质量安全风险评估(GJFP20210101);财政部和农业农村部：国家现代农业产业技术体系(CARS-13);中央级公益性科研院所基本科研业务费专项(Y2019XK17-02)

Research on the Application of a Balanced Sampling-Random Forest Early Warning Model for Aflatoxin Risk in Peanut

GUO Can^1,⁴(),YUE XiaoFeng^1,^3,⁴(),BAI YiZhen^1,^3,^4,⁵,ZHANG LiangXiao^1,^2,^3,⁴,ZHANG Qi^1,^2,^3,^4,⁵(),LI PeiWu^1,^2,^3,^4,⁵()

¹Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062
²Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Wuhan 430062
³National Agricultural Testing Benchmark Laboratory (Biotoxin), Wuhan 430062
⁴Laboratory of Risk Assessment for Oilseeds Products (Wuhan), Ministry of Agriculture and Rural Affairs, Wuhan 430062
⁵Quality Inspection and Test Center for Oilseeds Products, Ministry of Agriculture and Rural Affairs, Wuhan 430062

Received:2022-01-14 Accepted:2022-05-17 Online:2022-09-01 Published:2022-09-07
Contact: Qi ZHANG,PeiWu LI

摘要/Abstract

摘要：

【目的】花生极易受到黄曲霉毒素污染,本研究拟在前期创建的花生黄曲霉毒素平衡取样-随机森林预测预警模型基础上,通过系统性应用研究,明确模型主要技术参数与实际应用效果,为预测评估我国产后花生黄曲霉毒素风险提供关键技术支撑。【方法】利用前期建立的花生黄曲霉毒素平衡取样-随机森林风险预警模型,选择1个地理变量（纬度）和3个气候变量（收获前一个月8：00—20：00降水量、平均气压和日平均气温）作为模型数据的关键输入参数,预测2019和2020年我国花生主产区153个主产市（县）黄曲霉毒素污染风险。采用免疫亲和层析-高效液相色谱-荧光检测法,测定上述产区共2 164份花生的黄曲霉毒素含量,获得这些产区花生黄曲霉毒素污染数据。根据模型预测风险与实际测定结果,计算模型应用的准确率、精准率、灵敏度和假阳性率,明确应用效果。【结果】累计预测的153个市（县）中,共预测出125个低风险区,其中116个与实际测定评估结果相吻合,有9个实测评估高风险产区被预测误判为低风险产区（假阴性）。共预测出28个花生黄曲霉毒素污染高风险产区,其中15个与实际测定评估结果相吻合,有13个实测评估低风险产区被预测误判为高风险产区（假阳性）。该模型预测结果的总体准确率达到85.61%,假阴性率为8.49%,假阳性率为5.88%。【结论】花生黄曲霉毒素平衡取样-随机森林风险预警模型能够较好地预测出我国产后花生黄曲霉毒素污染风险,为科学指导我国产后花生收储与利用,减少黄曲霉毒素污染损失和保障农产品质量安全提供技术支撑。

关键词: 花生, 黄曲霉毒素, 平衡取样-随机森林, 预警模型, 分类矩阵

Abstract:

【Objective】 Peanuts are highly vulnerable to aflatoxin contamination. Based on the Balanced Sampling-Random Forest early warning model for aflatoxin contamination in peanut established previously, this study aimed to analyze the main technical parameters and practical application effects of the model through systematic application research, which could provide a critical technical support for risk prediction of aflatoxin in post-harvest peanuts in China.【Method】 The model was used to predict the aflatoxin contamination risk of 153 main peanut producing cities in China from 2019 to 2020 by selecting the data of one month before the peanut harvest, including one geographical variable (latitude) and three climatic variables (precipitation, average air pressure, and daily average temperature of 8:00-20:00) as the key input parameters of the model. The immunoaffinity chromatography-high performance liquid chromatography-fluorescence detection method was used to determine the aflatoxin content of 2 164 peanuts to obtain the aflatoxin contamination data areas. The accuracy, precision, sensitivity, and false-positive rate of the model were analyzed to clarify the application effect according to the predicted risk and the actual risk of the model. 【Result】 A total of 125 areas were predicted as low-risk areas of aflatoxin, of which 116 areas were consistent with the actual measurement results, but 9 high-risk producing areas were misjudged as low-risk areas (False negative). Meanwhile, 28 areas were predicted as high-risk areas of aflatoxin, of which 15 areas were consistent with the actual measurement results, but 13 low-risk producing areas were misjudged as high-risk producing areas (False positive). Therefore, the accuracy of the model was 85.61%, the false-negative rate was 8.49%, and the false-positive rate was 5.88%. 【Conclusion】 The application of the Balanced Sampling-Random Forest early warning model could predict the risk of aflatoxin contamination in peanuts, which provided the technical support for scientifically guiding the harvesting, storage and utilization in post-harvest peanuts in China, thereby reducing the loss of aflatoxin contamination and guaranteeing the quality and safety of agricultural products.

Key words: peanut, aflatoxin, balanced sampling-random forest, early warning model, classification matrix

郭灿,岳晓凤,白艺珍,张良晓,张奇,李培武. 花生黄曲霉毒素平衡取样-随机森林风险预警模型的应用研究[J]. 中国农业科学, 2022, 55(17): 3426-3436.

GUO Can,YUE XiaoFeng,BAI YiZhen,ZHANG LiangXiao,ZHANG Qi,LI PeiWu. Research on the Application of a Balanced Sampling-Random Forest Early Warning Model for Aflatoxin Risk in Peanut[J]. Scientia Agricultura Sinica, 2022, 55(17): 3426-3436.

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分类矩阵 Classification matrix	预测为阳性 Predicted as positive	预测为阴性 Predicted as negative
观察值为阳性 Originally classified as positive	T-P	F-N
观察值为阴性 Originally classified as negative	F-P	T-N

分类矩阵 Classification matrix	预测值为高风险地区 Predicted as high-risk areas	预测值为低风险地区 Predicted as low-risk areas
观察值为高风险地区 Originally classified as high-risk areas	8 (9.52%)	7 (8.33%)
观察值为低风险地区 Originally classified as low-risk areas	6 (7.14%)	63 (75.00%)

分类矩阵 Classification matrix	预测值为高风险地区 Predicted as high-risk areas	预测值为低风险地区 Predicted as low-risk areas
观察值为高风险地区 Originally classified as high-risk areas	7 (10.14%)	2 (2.89%)
观察值为低风险地区 Originally classified as low-risk areas	7 (10.14%)	53 (76.81%)

花生黄曲霉毒素平衡取样-随机森林风险预警模型的应用研究

Research on the Application of a Balanced Sampling-Random Forest Early Warning Model for Aflatoxin Risk in Peanut

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