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Research On THz Detection Of Pesticide

Posted on:2015-04-16Degree:MasterType:Thesis
Country:ChinaCandidate:J X MaFull Text:PDF
GTID:2181330467454891Subject:Detection Technology and Automation
Abstract/Summary:PDF Full Text Request
At present, in our country there are about more than600kinds of syntheticcompounds used as pesticides, such as insecticide, fungicide, herbicide, rodenticide,algicide, plant growth regulators and so on, and every year there are new pesticidesbeen developed. Because there are so many types of pesticides, it is impossible todistinguish all kinds of pesticides with only a sensory experience.In agro-industryproduction, the use of pesticides in play a positive role, but also lead to pollution anddamage to the ecological environment. At the same time, the pesticide residues inagricultural products and food has increasingly become a social problem affecting thesafety of human, food safety problem more and more get the attention of people.Terahertz time-domain spectroscopy is developed in recent years as a new type ofspectral analysis technology, the technology has the following advantages:1)The THzband lie between microwave and infrared. The low frequency vibration modes ofOrganic molecules fall within the scope of the band.2) THZ waves can penetrate manynonpolar molecules material, such as plastic products, paper products, cloth and so on.It can test the items in the package.3) Because the photon energy is low, the reactivemolecules hardly produce light ionization effect;4) Terahertz source stability, theinfluence of thermal radiation in the environment is very small, and its wavelength islonger, weak scattering effect;5) Using coherent measurement technology, can get theterahertz instantaneous electric field amplitude and phase information at the same time(traditional spectroscopy for phase information cannot be directly). Based on the abovefeatures, the technology in rapid accurate detection of pesticide has a broad prospect.In this paper Terahertz time-domain spectroscopy is applied, combining withdensity functional theory and pattern recognition algorithm, spectrum qualitativeanalysis and classification of four pesticides have been studied. In this paper, the mainwork is as follows: (1) Using terahertz time-domain spectroscopy system detected and analyzed theterahertz spectrum of four kinds of commonly used pesticide and three kinds ofvegetables, results showed that the four kinds of pesticides in the range0.3to2.2THzhas the characteristics of absorption, and the absorption peak position are not the same;Within the scope of0.3to1.6THz, the carrots has an absorption peak at1.44THz, theother two kinds of vegetables have no absorption peak, the absorption spectra of thethree kinds of vegetables and the absorption spectrum of the four kinds of pesticidesare not the same.(2)The geometry optimization and frequency calculation of the above fourpesticides were carried out based on the density functional theory (DFT), performedwith the software package Gaussian03using B3LYP method and6-311G(d, p) basisset. The comparisons of the experimental absorption spectrum and theoreticalabsorption spectrum were displayed. And the vibration modes of the absorptionpeakmeached were identified.(3) Base on the above research, applied two kinds of pattern recognitionalgorithm for terahertz absorption spectra of four kinds of pesticide to achieveautomatic classification:1) Using principal component analysis method extract theprincipal components of the absorption spectra of four kinds of pesticide. We put thefirst, the second and the third principal component as the characteristic vector, usingC4.5decision tree method to try to classify the kinds of pesticides.2) Using supportvector machine (SVM) method, the above characteristic vector and the absorptionspectra of four kinds of pesticides are successfully classified.In the end of the paper, concluses the works and discusses the direction of furtherresearch and the problem needed to be improved.
Keywords/Search Tags:terahertz time domain spectroscopy, pesticide, absorption coefficient, density functional, pattern recognition
PDF Full Text Request
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