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》2017年碩士論文

【摘要】：藥品關系人民健康,真假藥鑒別和藥品種類鑒別,在藥品監(jiān)督中有強烈的應用需求。傅里葉近紅外光譜儀是一種光機電結(jié)合的精密測量裝置,具有現(xiàn)場、快速、無損檢測等優(yōu)點,結(jié)合統(tǒng)計學或化學計量學方法,常用于各類物理化學值的測量,也于近年成為我國藥品流動檢測車中的必配裝備。在藥品鑒別應用中,上百臺儀器常同時使用,因此本文研究臺間差產(chǎn)生的原因并給出模型傳遞方法,并重點研究兩類和多類的藥品鑒別。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用的基本流程,然后介紹了小波變換光譜預處理方法,以及自編碼網(wǎng)絡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械結(jié)構(gòu),并分析了光譜檢測誤差產(chǎn)生的機械和環(huán)境因素。研究了將小波變換光譜預處理方法與一元線性回歸直接標準化算法(SLRDS)結(jié)合的模型傳遞方法,實驗結(jié)果表明,引入小波變換可更好地消除儀器機械和環(huán)境因素帶來的測量誤差,提升模型傳遞效果。本文提出一種稀疏降噪自編碼結(jié)合高斯過程的藥品鑒別二分類算法wSDAGsM。該算法首先對光譜數(shù)據(jù)進行一維小波連續(xù)變換,然后應用稀疏降噪自編碼結(jié)合高斯過程進行二分類。實驗結(jié)果表明,本文提出的建模方法wSDAGsM,對比BP神經(jīng)網(wǎng)絡等算法,在分類準確率及穩(wěn)定性方面,均取得了更優(yōu)的結(jié)果。同時,實驗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(SVM)的藥品鑒別二分類和多分類算法wSDAMRBF。該算法首先對光譜數(shù)據(jù)進行一維小波連續(xù)變換,然后用稀疏降噪自編碼結(jié)合SVM進行二分類和多分類。本文對wSDAGSM和wSDAMRBF算法開展了對比實驗研究,結(jié)果表明,兩個算法都能較好地用于藥品鑒別,相對而言,wSDAMRBF算法在分類準確率和結(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.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：O657.33;TQ460.72

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