小波變換-主成分回歸分光光度法同時測定感冒液成分的研究

化工論文編號:HG097  論文字數(shù):11209,頁數(shù):34 
 
目  錄
 中文摘要...............................................................Ⅰ
 英文摘要...............................................................II
 目錄...................................................................III
 1. 緒論.................................................................1
 1.1 小波分析.........................................................1
 1.2 小波變換在化學計量學中的應(yīng)用.....................................1
 1.3 主成分回歸.......................................................2
 1.4 本研究的主要思路與方法...........................................3
 2. 多組分同時測定的原理及實驗方法.......................................4
 2.1 計算分光光度法測定多組分的原理...................................4
 2.2 實驗方法與技術(shù)...................................................4
 3. 算法基本原理.........................................................7
 3.1 小波變換基本原理.................................................7
 3.2 PCR數(shù)學原理.....................................................8
 3.3 WT-PCR算法......................................................9
 4. 實驗部分............................................................11
 4.1 實驗儀器........................................................11
 4.2 實驗試劑........................................................11
 4.3 實驗方法........................................................11
5. 結(jié)果與討論.........................................................13
5.1 吸收曲線與測定波長的選擇.......................................13
5.2 樣品穩(wěn)定性分析.................................................14
5.3 訓練樣本的確定.................................................14
5.4 最佳小波基和截取尺度的選擇.....................................16
5.5 主成分數(shù)的確定.................................................17
 5.6 標準樣品試驗...................................................19
 5.7 方法適用性試驗.................................................19
 5.8 加標回收試驗...................................................20
 5.9 未知樣品預(yù)報...................................................21
 6. 總結(jié)與展望..........................................................22
 致謝...................................................................23
 參考文獻...............................................................24
 附錄MATLAB相關(guān)程序....................................................26

摘 要：在多組分樣品的分光光度法測定中，吸收曲線常會發(fā)生不同程度的重疊,采用一般分光光度法難于定量每一組分，而近年來發(fā)展起來的小波變換（WT）已成為信息科學中的一個新興研究領(lǐng)域，越來越多地應(yīng)用于化學研究領(lǐng)域中各種復(fù)雜的、非線性的問題，也取得了很好效果。本文根據(jù)小波變換具有將信號分頻的特點，采用db9小波在4尺度低頻系數(shù)構(gòu)造分析矩陣進行主成分回歸(PCR)，預(yù)測結(jié)果優(yōu)于原始數(shù)據(jù)全譜模型。利用小波變換與主成分回歸相結(jié)合的一種多元校正算法對感冒液中對乙酰氨基酚、愈創(chuàng)木酚甘油醚、咖啡因和撲爾敏四組分體系進行了同時測定。加標回收率介于91.67-102.37%，結(jié)果基本滿意。使用小波系數(shù)進行PCR建模，不僅可以有效地減小主成分向量殘留噪聲所引起的誤差，而且較大地壓縮了數(shù)據(jù)量，顯著提高多元校正準確性，提高了預(yù)測精度。

關(guān)鍵詞：小波變換；主成分回歸；紫外分光光度法；多組分同時測定

 The Study of Simultaneous Spectrophotometric Determination for Cough Syrup by Principal Component Regression
 Based on Wavelet Transform
Abstract: In multi-component spectrophotometric determination， the absorption curve offen overlaps in varying degrees , It is difficult to quantitate each component using spectrophotometry generally, wavelet transform(WT)which is developed in recent years has become an emerging field of research in information science, and more and more used in the field of chemical research in a variety of complex, nonlinear problems, also achieved good results. In this paper, based on wavelet transform in accordance with the signal frequency characteristics, using wawelet db9 at level 4 to reconstruct a standard low-frequency coefficient for the original absorbance data to structure analysis matrix to rincipal component regression (PCR) regression analysis, forecasting results is better than the original entire spectrum model.Using a algorithm for multivariate calibration which was combining the WT and PCR technique applied to the simultaneous determination the Cough Syrup which has four components: acetaminophen, guuaifenesin, caffeine, p-aminophenol.the recovery ranged from 91.67% to 102.37% .the result is basically satisfied. Using wavelet coefficients to build PCR model, can not only effectively reduce the principal component vector of residual errors caused by noise , but also can greatly reduce the volume of data, significantly improve the accuracy of multivariate calibration to improve the prediction accuracy.

 Keywords: Wavelet transforrrmtion；PCR；UV spectrophotometry；Simultaneous determination of multiple components；