Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (8): 1951-1956.doi: 10.13229/j.cnki.jdxbgxb20210176

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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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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

CLC Number: 

  • S481.8

Fig.1

Comparison of accuracy of different feature selection algorithms"

Table 1

Classification results of different feature selection algorithms"

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

Fig.2

Importance ranking of features selected by CatBoost algorithm"

Fig.3

Accuracy and selected feature subset box diagram of six feature selection algorithms"

Fig.4

PCA analysis results of sensor response characteristic"

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