Detection of Permethrin Pesticide Residue in Teas Based on Spectrum Fuorescence and GA-RBF Neural Network

WANG Shu-tao; YUAN Yuan-yuan; WANG Yu-tian; ZHAO Xu; ZHANG Ya-ji; NIU Kai-zeng

doi:10.3788/fgxb20163710.1267

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Detection of Permethrin Pesticide Residue in Teas Based on Spectrum Fuorescence and GA-RBF Neural Network

更新时间：2020-08-12

- Detection of Permethrin Pesticide Residue in Teas Based on Spectrum Fuorescence and GA-RBF Neural Network
- Chinese Journal of Luminescence Vol. 37, Issue 10, Pages: 1267-1274(2016)
- 作者机构：
  
  燕山大学河北省测试计量技术及仪器重点实验室,河北秦皇岛,066000
- 作者简介：
- 基金信息：
- DOI：10.3788/fgxb20163710.1267
  CLC： O433.4
- Received：22 April 2016，
  
  Revised：15 June 2016，
  
  Published：05 October 2016
- 稿件说明：
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王书涛, 苑媛媛, 王玉田等. 基于三维荧光与GA-RBF神经网络对茶叶中氯菊酯农药残留的检测[J]. 发光学报, 2016,37(10): 1267-1274

WANG Shu-tao, YUAN Yuan-yuan, WANG Yu-tian etc. Detection of Permethrin Pesticide Residue in Teas Based on Spectrum Fuorescence and GA-RBF Neural Network[J]. Chinese Journal of Luminescence, 2016,37(10): 1267-1274
王书涛, 苑媛媛, 王玉田等. 基于三维荧光与GA-RBF神经网络对茶叶中氯菊酯农药残留的检测[J]. 发光学报, 2016,37(10): 1267-1274 DOI： 10.3788/fgxb20163710.1267.

WANG Shu-tao, YUAN Yuan-yuan, WANG Yu-tian etc. Detection of Permethrin Pesticide Residue in Teas Based on Spectrum Fuorescence and GA-RBF Neural Network[J]. Chinese Journal of Luminescence, 2016,37(10): 1267-1274 DOI： 10.3788/fgxb20163710.1267.

摘要

采用FS920稳态荧光光谱仪对绿茶和铁观音这两种不同品种茶叶的氯菊酯溶液的荧光光谱特性进行了分析，发现这两种茶叶的荧光峰均位于

=（390~410）/675 nm，氯菊酯的荧光峰

=300/330 nm。为了准确测定这两种茶叶中氯菊酯农药残留的含量，采用遗传算法优化的径向基函数神经网络对其进行了分析，当训练到74次时，均方差精度达到10

-3

，绿茶、铁观音的氯菊酯溶液预测样本的平均回收率分别为99.35%和98.89%，平均相对标准偏差分别为1.25%和1.21%。与建立的径向基函数神经网络模型进行了对比，结果表明三维荧光分析技术与遗传算法优化的径向基函数神经网络相结合能够较好地检测出茶叶中氯菊酯农药残留的含量，检测灵敏度大大提高，检出限范围广，可达0.004 8~24 mg/kg，远低于欧盟规定的茶叶中氯菊酯最高残留限量0.1 mg/kg，为检测农药残留提供了一种快速简便的新方法。

Abstract

The fluorescence spectra of permethrin in green tea and tieguanyin were studied. The fluorescence characteristic peaks of green tea and tieguanyin existed at

=(390-410)/675 nm

while the permethrin fluorescence characteristic peak existed at

=300/330 nm. To determine the content of permethrin in teas

a new method of a radial basis function (RBF) neural network based on a genetic algorithm (GA) was proposed. When the training is to 74

the precision of the mean square deviation reaches 10

-3

. The average forecast recovery rates of permethrin in green tea and tieguanyin are 99.35% and 98.89%

the average relative standard deviation are 1.25% and 1.21%

and the detection limit range is from 0.004 8 to 24 mg/kg

which is far lower than that of the EU rules of permethrin in tea maximum residue limits standards. Through the contrast of the RBF neural network model

it is found that the three-dimensional fluorescence analysis technology combined with GA-RBF neural network can predict the content of permethrin pesticide residue in teas quickly and easily

the detection sensitivity and the precision are higher.

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Keywords

references

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