吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (8): 1951-1956.doi: 10.13229/j.cnki.jdxbgxb20210176

• 农业工程·仿生工程 • 上一篇    

基于多传感器优化的农药残留快速检测新方法

李佩泽1,2(),赵世舜1,翁小辉3,蒋鑫妹4,崔洪博4,乔建磊4,常志勇5,6()   

  1. 1.吉林大学 数学学院，长春 130012
    2.吉林大学 人工智能学院，长春 130012
    3.吉林大学 机械与航空航天工程学院，长春 130022
    4.吉林农业大学 园艺学院，长春 130118
    5.吉林大学 工程仿生教育部重点实验室，长春 130022
    6.吉林大学 生物与农业工程学院，长春 130022
  • 收稿日期:2021-03-08 出版日期:2022-08-01 发布日期:2022-08-12
  • 通讯作者: 常志勇 E-mail:lipz18@mails.jlu.edu.cn;zychang@jlu.edu.cn
  • 作者简介:李佩泽（1996-），女，博士研究生.研究方向：仿生数据分析. E-mail： lipz18@mails.jlu.edu.cn
  • 基金资助:
    国家自然科学基金项目(51875245);吉林省科技发展计划项目(20200403038SF);吉林省产业技术研究与开发专项项目(2020C023-6);吉林省教育厅“十三五”科学技术项目(JJKH20211120KJ);吉林省人才开发基金项目(2020015)

A new method for rapid detection of pesticide residues based on multi⁃sensor optimization

Pei-ze LI1,2(),Shi-shun ZHAO1,Xiao-hui WENG3,Xin-mei JIANG4,Hong-bo CUI4,Jian-lei QIAO4,Zhi-yong CHANG5,6()   

  1. 1.College of Mathematics，Jilin University，Changchun 130012，China
    2.School of Artificial Intelligence，Jilin University，Changchun 130012，China
    3.School of Mechanical and Aerospace Engineering，Jilin University，Changchun 130022，China
    4.College of Horticulture，Jilin Agricultural University，Changchun 130118，China
    5.Key Laboratory of Bionic Engineering，Ministry of Education，Jilin University，Changchun 130022，China
    6.College of Biological and Agricultural Engineering，Jilin University，Changchun 130022，China
  • Received:2021-03-08 Online:2022-08-01 Published:2022-08-12
  • Contact: Zhi-yong CHANG E-mail:lipz18@mails.jlu.edu.cn;zychang@jlu.edu.cn

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摘要：

提出了一种基于CatBoost算法的传感器阵列优化策略。采用自行研制的基于仿生嗅觉的电子鼻测试系统，检测蒲公英上残留的农药敌百虫，提取蒲公英样本的响应特征信息，对传感器阵列进行多特征数据融合。使用CatBoost算法对数据矩阵进行特征选择，优化后的传感器数量从12个减少到3个，准确率从91.69%提高到98.03%，减少了约88%的特征值，优于相关系数、递归消除和其他常用算法，解决了传感器繁多、数据冗余的问题，大大提高了检测精度。结果表明：在蒲公英敌百虫残留检测上使用CatBoost算法可提高电子鼻的鉴别能力。

关键词: 农药学, CatBoost, 电子鼻, 特征选择, 多传感器

Abstract:

A sensor array optimization strategy based on CatBoost algorithm was proposed. Using the self-developed electronic nose testing system based on bionic olfaction，the residual trichlorfon on dandelion was detected，the response characteristic information of the dandelion sample was extracted， and the multi-characteristic data fusion on the sensor array was performed. The CatBoost algorithm was used to perform feature selection on the data matrix. The number of optimized sensors was reduced from 12 to 3， the accuracy rate was increased from 91.69% to 98.03%， and the number of features was reduced by 88%， which was better than correlation coefficient， recursive elimination and other commonly used algorithms. The problem of multiple sensors and data redundancy was solved， and the detection accuracy was greatly improved. The results show that the use of CatBoost algorithm in the detection of trichlorfon residues on dandelion can improve the identification ability of the electronic nose.

Key words: pesticide science, CatBoost, electronic nose, feature selection, multi-sensor

中图分类号: 

  • S481.8

图1

不同特征选择算法准确率比较"

表1

不同特征选择算法分类结果"

特征选择方法原始数据CatBoostReliefF相关系数随机森林递归消除逻辑回归递归消除支持向量机递归消除
特征数量108136668405018
传感器的数量12312129116
准确率0.91690.98030.96060.94060.97630.95650.9685
准确率P值0.0072-0.22760.01630.77270.13490.4573
敏感度0.89780.97220.94890.94110.97440.94560.9578
敏感度P值0.0129-0.29480.12370.90150.18540.5530
特异度0.95570.98920.97890.96970.98810.97820.9831
特异度P值0.0077-0.23420.02740.87870.16610.4795

图2

CatBoost算法选择特征的重要度排序"

图3

6种特征选择算法准确率和选择的特征子集箱线图"

图4

传感器响应特征值PCA分析结果"

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