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Study of Oil Identification Technique Based on Three-dimensional Fluorescence Spectra

Author: ZhangYanLin
Tutor: WangYuTian
School: Yanshan University
Course: Measuring Technology and Instruments
Keywords: Spectroscopy Oil Identification Principal Component Analysis BP neural network
CLC: X832
Type: Master's thesis
Year: 2010
Downloads: 118
Quote: 0
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Abstract


Water quality in the detection of mineral oil by the extensive attention , quickly and accurately identify the type of mineral oil in water , has an important significance for the implementation of the prevention and control of pollution of the water environment . Sensitive fluorescence analysis for environmental testing requirements for a variety of three-dimensional fluorescence spectrum of mineral oil and water mixed solution , the use of neural networks for pattern recognition of the basic principles of principal component analysis and BP neural network combined proposed mineral oil the three-dimensional fluorescence spectra to identify the program , and system design , to establish the basic framework of the model . Select the characteristic parameters of the three-dimensional fluorescence spectrum of mineral oil , composed of the original feature vector preprocessed using principal component analysis , and then select the principal component by BP neural networks to achieve Oil Identification . This article discusses the basic principles and characteristics of the fluorescence measurement method , based on the experimental two-dimensional fluorescence spectra of several oil pollution , the limitations of the traditional two-dimensional fluorescence spectrometry . Fluorescence measurement system is designed , using a pulsed xenon lamp as an excitation light source, using the diffraction grating as a spectroscopic system , the photomultiplier tube combined the IVC102 precision integrating amplifier , to detect the weak signal . The proposed three-dimensional fluorescence spectrum of mineral oil authentication scheme and system design , to establish the basic framework of the model . The specific steps : principal component analysis extracted three fluorescence spectra characteristic parameters , data standardization , and then principal component input neural networks for pattern recognition , the output category of mineral oil .

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CLC: > Environmental science, safety science > Environmental Quality Assessment and Environmental Monitoring > Environmental monitoring > Water quality monitoring
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