發(fā)布時(shí)間：2018-07-01 15:43

  本文選題：傅里葉近紅外光譜儀 + 模型傳遞��； 參考：《北京郵電大學(xué)》2017年碩士論文

【摘要】：藥品關(guān)系人民健康,真假藥鑒別和藥品種類鑒別,在藥品監(jiān)督中有強(qiáng)烈的應(yīng)用需求。傅里葉近紅外光譜儀是一種光機(jī)電結(jié)合的精密測(cè)量裝置,具有現(xiàn)場(chǎng)、快速、無(wú)損檢測(cè)等優(yōu)點(diǎn),結(jié)合統(tǒng)計(jì)學(xué)或化學(xué)計(jì)量學(xué)方法,常用于各類物理化學(xué)值的測(cè)量,也于近年成為我國(guó)藥品流動(dòng)檢測(cè)車中的必配裝備。在藥品鑒別應(yīng)用中,上百臺(tái)儀器常同時(shí)使用,因此本文研究臺(tái)間差產(chǎn)生的原因并給出模型傳遞方法,并重點(diǎn)研究?jī)深惡投囝惖乃幤疯b別。本文首先介紹近紅外光譜儀的分類和傅里葉變換的工作原理,以及近紅外光譜分析應(yīng)用的基本流程,然后介紹了小波變換光譜預(yù)處理方法,以及自編碼網(wǎng)絡(luò)等光譜特征提取方法的基本原理。本文接著介紹了傅里葉近紅外光譜儀的核心——邁克爾遜干涉儀的機(jī)械結(jié)構(gòu),并分析了光譜檢測(cè)誤差產(chǎn)生的機(jī)械和環(huán)境因素。研究了將小波變換光譜預(yù)處理方法與一元線性回歸直接標(biāo)準(zhǔn)化算法(SLRDS)結(jié)合的模型傳遞方法,實(shí)驗(yàn)結(jié)果表明,引入小波變換可更好地消除儀器機(jī)械和環(huán)境因素帶來(lái)的測(cè)量誤差,提升模型傳遞效果。本文提出一種稀疏降噪自編碼結(jié)合高斯過(guò)程的藥品鑒別二分類算法wSDAGsM。該算法首先對(duì)光譜數(shù)據(jù)進(jìn)行一維小波連續(xù)變換,然后應(yīng)用稀疏降噪自編碼結(jié)合高斯過(guò)程進(jìn)行二分類。實(shí)驗(yàn)結(jié)果表明,本文提出的建模方法wSDAGsM,對(duì)比BP神經(jīng)網(wǎng)絡(luò)等算法,在分類準(zhǔn)確率及穩(wěn)定性方面,均取得了更優(yōu)的結(jié)果。同時(shí),實(shí)驗(yàn)也表明小波變換可以較好地消除光譜噪聲。本文提出一種稀疏降噪自編碼結(jié)合支持向量機(jī)(SVM)的藥品鑒別二分類和多分類算法wSDAMRBF。該算法首先對(duì)光譜數(shù)據(jù)進(jìn)行一維小波連續(xù)變換,然后用稀疏降噪自編碼結(jié)合SVM進(jìn)行二分類和多分類。本文對(duì)wSDAGSM和wSDAMRBF算法開展了對(duì)比實(shí)驗(yàn)研究,結(jié)果表明,兩個(gè)算法都能較好地用于藥品鑒別,相對(duì)而言,wSDAMRBF算法在分類準(zhǔn)確率和結(jié)果穩(wěn)定性更優(yōu)。
[Abstract]:Drugs are closely related to people's health, genuine and false drugs and drug types, which have a strong demand for application in drug supervision. Fourier near Infrared Spectrometer (FNIR) is a kind of precision measuring device combined with light and electromechanical. It has the advantages of field, fast and nondestructive testing. It is often used in the measurement of various physical and chemical values combined with statistics or chemometrics. In recent years, it has become the necessary equipment in the mobile drug testing vehicle in China. In drug identification applications, hundreds of instruments are often used at the same time, so this paper studies the causes of the difference between stations and gives the method of model transfer, and focuses on the identification of two or more kinds of drugs. This paper first introduces the classification of near infrared spectrometer and the working principle of Fourier transform, and the basic flow of near infrared spectrum analysis and application, then introduces the pretreatment method of wavelet transform spectrum. And the basic principle of spectral feature extraction method such as self-coding network. In this paper, the mechanical structure of Michelson interferometer, which is the core of Fourier near infrared spectrometer, is introduced, and the mechanical and environmental factors of spectrum detection error are analyzed. The model transfer method which combines wavelet transform spectral pretreatment method with linear regression direct standardization algorithm (SLRDS) is studied. The experimental results show that wavelet transform can better eliminate the measurement errors caused by mechanical and environmental factors. Improved model delivery effect. In this paper, a novel two-classification algorithm for drug identification, wSDAGsMbased on sparse noise reduction self-coding and Gao Si process, is proposed. The algorithm firstly performs one-dimensional wavelet continuous transform for spectral data and then uses sparse noise reduction self-coding and Gao Si process to classify the spectral data. The experimental results show that the proposed modeling method wSDAGsM, compared with BP neural network, has better results in classification accuracy and stability. At the same time, the experiment also shows that wavelet transform can eliminate spectral noise. In this paper, a sparse denoising self-coding and support vector machine (SVM) algorithm for drug identification is proposed. The algorithm firstly performs one-dimensional wavelet continuous transform for spectral data, and then uses sparse denoising self-coding and SVM to carry out two-classification and multi-classification. In this paper, a comparative study of wSDAGSM and wSDAMRBF algorithms is carried out. The results show that both algorithms can be used for drug identification, and the classification accuracy and stability of wSDAMRBF algorithm are better than that of wSDAMRBF algorithm.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O657.33;TQ460.72

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