博碩士論文 105223037 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:13 、訪客IP:18.218.129.100
姓名 ?文韶(Wen-Shao Pang)  查詢紙本館藏   畢業系所 化學學系
論文名稱 氣相層析自動化控制與積分演算法應用於空氣中有機污染物連續監測
相關論文
★ 有機薄膜電晶體材料三併環及四併環噻吩衍生物之開發★ 以逆吹式氣相層析法分析氣體成份
★ 氣相層析法應用於工業排放連續監測★ 煙道氣揮發性有機化合物連續監測方法開發
★ 自製新型除水及熱脫附濃縮裝置用於GC/MS線上分析揮發性有機汙染物★ 觸媒式非甲烷總碳氫分析儀開發與驗證
★ 自製除水器及熱脫附儀用於線上GC/MS/FID揮發性有機污染物之分析★ 大氣及水樣中揮發性有機氣體自動化分析技術之建立及應用
★ VOC前濃縮與預警系統之建構★ 建立自動化甲烷連續量測系統與其在指示大氣輻射冷卻之應用
★ 臭氧前趨物連續監測與臭氧生成之光化學探討★ 以近連續方式量測空氣中甲烷與異戊二烯及其生成之季節性探討
★ 自行架設光化學測站與商業化儀器平行比對及所得資料初步分析★ 近地表臭氧前驅物分析之前濃縮技術改良
★ 自動化噴霧捕捉分析系統之建立與研究★ 大體積固相微萃取水中揮發性有機污染物
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 本研究目的是建立一套自動化軟體系統, 能夠針對空氣中揮發性有機化合物 (Volatile Organic Compounds, VOCs)進行連續監測。此系統包含兩大部分,分別為氣相層析儀的程序自動控制與層析訊號處理。
在程序自動控制部分,系統包含了氣體閥門之控制、訊號擷取、溫度控制,此外亦建立了詢問型的程序控制架構,在此架構下,軟體可以進行四種不同的執行模式,分別為周界模式 (Ambient Mode)、標準氣體模式 (Cylinder Mode)、空白模式 (Blank Mode)、客製化模式 (Custom Mode),模式間可以任意的切換,並且可以使這四種模式照使用者需求進行排列並執行序列模式 (Sequence Mode),達到連續監測時的自動化校準與清理之目的。在層析訊號處理方面,本研究建立了兩套層析積分演算法,斜率法與模型法,前者主要以層析圖譜之微分值作為判斷層析峰位置與範圍之依據,並且針對層析峰之範圍進行面積積分,而對於共析層析峰則是以垂直切割 (Drop)的方式進行解析,後者模型法則以非線性曲線擬合法,並套用層析峰模型 (Peak Mode)以模擬層析峰形,最後根據所解析的層析峰進行面積積分。
為驗證演算法之成效,本研究以環保署NIEA A505.12B方法之標準氣體進行測試,此氣體內含54種碳數介於C2 – C12 之VOCs。測試結果顯示在上述兩種演算法下有90%以上之物種其RSD小於1 %,而約90%之物種之R2值亦優於0.999,顯示本研究之演算法有良好的再線性與定量能力,並且具備接近商業化軟體之成效。
我們亦將此軟體系統實際應用於非甲烷總碳氫分析(Total Non-methane Hydrocarbons, tNMHCs ) 連續監測上,使用本實驗室所設計開發之火焰離子 (FID) 偵測式總碳氫分析儀 (THC)。測試結果顯示該分析儀於濃度1.2 - 72 ppm間的甲烷與總碳氫 (Total Hydrocarbons, THC)之檢量線的R2皆大於0.995,此外在標準氣體的連續測試上亦成功驗證了此軟體控制系統在運作時的穩定性。
摘要(英) The objective of this research is to develop a software solution to automate a chromatographic system to monitor ambient volatile organic compounds (VOCs). This software solution consists of two components, the process automation and the data processing algorithm.
In the process automation part, functionalities of gas-valve control, signal acquisition, and temperature control were designed. An inquiry-type architecture was constructed for process automation. Under this architecture, the software can perform four different execution modes and can arbitrarily switched between the four modes. These modes include Ambient Mode, Cylinder Mode, Blank Mode and Custom mode. Moreover, the modes can be arranged to execute in sequence according to the user’s needs, with an aim to perform both the concentration calibration and system cleaning in continuous cycles.
For the data processing algorithm, two methods were developed to integrate chromatographic peaks, i.e., the slope method and the model method. The slope method uses differentials of the chromatographic signals as the basis for detecting the occurrence and the range of a chromatograph peak for area integration. In addition, co-eluted chromatographic peaks can be processed by the so called “drop-cut” method in the slope method. For the model method, it uses the nonlinear curve fitting method to simulate chromatographic peaks. Subsequently, peak area integration is performed on the modeled chromatographic peaks.
To assess the effectiveness of the algorithm, the standard mixture of an EPA method NIEA E505.12B containing 54 VOCs from C2 – C12 was analyzed. Our results show that more than 90% of the species are better than 1% RSD, and about 90% of species show linearity (R2) greater than 0.999. As a result, both precision and accuracy can be demonstrated by our developed data processing algorithm which is close to the performance of a commercial software as the benchmark.
When applied the software package to the analyzer of total hydrocarbons (THC) with flame ionization detection (FID) previously developed by our laboratory to analyze total non-methane hydrocarbons (tNMHCs). We found that the R2 values for methane and THC were greater than 0.995 between 1.2 - 72 ppm in concentration. Moreover, the analyzer equipped with the software was able to show long-term stability by continuous analyzing a standard gas.
關鍵字(中) ★ 自動控制
★ 層析積分演算法
★ 訊號處理
★ 層析峰模型
★ 非甲烷總碳氫
關鍵字(英) ★ Automation control
★ Chromatography peak detection algorithm
★ Data processing
★ Peak Model
★ Non-methane hydrocarbons(tNMHCs)
論文目次 摘要 i
Abstract iii
謝誌 v
目錄 vii
圖目錄 xi
表目錄 xix
第一章 前言 1
1-1 研究緣起 1
1-2 研究目的 2
1-3 程式語言簡介 4
1-3-1 LabVIEW程式語言 4
1-3-2 Python程式語言 8
第二章 層析圖譜數據處理方法文獻回顧與介紹 11
2-1 訊號平滑與基線校正 13
2-2 層析峰找尋與積分 23
2-3 層析峰漂移校準 38
第三章 自動化控制系統之流程架構 45
3-1 程式流程 46
3-2 控制系統架構 49
3-3 流線型流程控制 59
3-4 陣列型流程控制 63
3-5 詢問型流程控制 67
3-6 序列模式之建立 71
第四章 層析圖譜積分演算法之建立 75
4-1 演算法流程 75
4-2 平滑濾波器 77
4-3 尋找層析峰 78
4-4 偽層析峰判定 81
4-5 共析層析峰定義 84
4-6 斜率法 88
4-7 模型法 91
4-8 自動物種判別方法 98
4-9 自動建立檢量線 100
第五章 系統應用 105
5-1 積分演算法參數探討 105
5-1-1 模擬層析圖之建立 105
5-1-2 平滑影響探討 106
5-1-3 訊雜比影響探討 111
5-2 積分演算法實測結果 113
5-2-1 分析數據 113
5-2-2 結果與討論 117
5-3 層析峰模型之比較 123
5-4 非甲烷總碳氫監測系統 128
5-4-1 非甲烷總碳氫分析系統 128
5-4-2 儀器與設備 128
5-4-3 檢量線測試 133
5-4-4 連續監測測試 135
5-4-5 監測系統實地實測 136
第六章 結論 143
參考資料 145
參考文獻 1. TIOBE, TIOBE Index for May 2018. 2018.
https://www.tiobe.com/tiobe-index/
2. O′Callaghan, S.; De Souza, D. P.; Isaac, A.; Wang, Q.; Hodkinson, L.; Olshansky, M.; Erwin, T.; Appelbe, B.; Tull, D. L.; Roessner, U.; Bacic, A.; McConville, M. J.; Liki?, V. A., PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools. BMC Bioinformatics 2012, 13 (1), 115.
3. Jansen, B. C.; Falck, D.; de Haan, N.; Hipgrave Ederveen, A. L.; Razdorov, G.; Lauc, G.; Wuhrer, M., LaCyTools: A Targeted Liquid Chromatography–Mass Spectrometry Data Processing Package for Relative Quantitation of Glycopeptides. Journal of Proteome Research 2016, 15 (7), 2198-2210.
4. Titaley, I. A.; Ogba, O. M.; Chibwe, L.; Hoh, E.; Cheong, P. H. Y.; Simonich, S. L. M., Automating data analysis for two-dimensional gas chromatography/time-of-flight mass spectrometry non?targeted analysis of comparative samples. Journal of Chromatography A 2018, 1541, 57-62.
5. Yu, Y.-J.; Xia, Q.-L.; Wang, S.; Wang, B.; Xie, F.-W.; Zhang, X.-B.; Ma, Y.-M.; Wu, H.-L., Chemometric strategy for automatic chromatographic peak detection and background drift correction in chromatographic data. Journal of Chromatography A 2014, 1359, 262-270.
6. O′Haver, T., A Pragmatic Introduction to Signal Processing with application in scientific measurement. 2017.
7. Waters, Empower Software Data Acquisition and Processing :Theory Guide. 2002.
8. Thermo, Intelligent Integratopn Using Cobra and SmartPeaks. 2016.
9. Vaz, F. A. S.; Neves, L. N. O.; Marques, R.; Sato, R. T.; Oliveira, M. A. L., Chromophoreasy, an Excel-Based Program for Detection and Integration of Peaks from Chromatographic and Electromigration Techniques. Journal of the Brazilian Chemical Society 2016, 27, 1899-1911.
10. Savitzky, A.; Golay, M. J. E., Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry 1964, 36 (8), 1627-1639.
11. Schafer, R. W., What Is a Savitzky-Golay Filter? [Lecture Notes]. IEEE Signal Processing Magazine 2011, 28 (4), 111-117.
12. Vivo-Truyols, G.; Torres-Lapasio, J. R.; van Nederkassel, A. M.; Vander Heyden, Y.; Massart, D. L., Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals: Part I: Peak detection. Journal of Chromatography A 2005, 1096 (1), 133-145.
13. Lytle, F. E.; Julian, R. K., Automatic Processing of Chromatograms in a High-Throughput Environment. Clinical Chemistry 2016, 62 (1), 144.
14. H C Eilers, P.; F M Boelens, H., Baseline Correction with Asymmetric Least Squares Smoothing. 2005.
15. Zhang, Z.-M.; Chen, S.; Liang, Y.-Z., Baseline correction using adaptive iteratively reweighted penalized least squares. Analyst 2010, 135 (5), 1138-1146.
16. Komsta, ?., Comparison of Several Methods of Chromatographic Baseline Removal with a New Approach Based on Quantile Regression. Chromatographia 2011, 73 (7), 721-731.
17. Johnsen, L. G.; Skov, T.; Houlberg, U.; Bro, R., An automated method for baseline correction, peak finding and peak grouping in chromatographic data. Analyst 2013, 138 (12), 3502-3511.
18. Liu, X.; Zhang, Z.; Sousa, P. F. M.; Chen, C.; Ouyang, M.; Wei, Y.; Liang, Y.; Chen, Y.; Zhang, C., Selective iteratively reweighted quantile regression for baseline correction. Analytical and Bioanalytical Chemistry 2014, 406 (7), 1985-1998.
19. 訊華股份有限公司, SISC層析儀機分數據處理系統操作手冊(上).
20. 訊華股份有限公司, SISC層析儀機分數據處理系統操作手冊(下).
21. Vivo-Truyols, G.; Torres-Lapasio, J. R.; van Nederkassel, A. M.; Vander Heyden, Y.; Massart, D. L., Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals: Part II: Peak model and deconvolution algorithms. Journal of Chromatography A 2005, 1096 (1), 146-155.
22. Dixon Sarah, J.; Brereton Richard, G.; Soini Helena, A.; Novotny Milos, V.; Penn Dustin, J., An automated method for peak detection and matching in large gas chromatography?mass spectrometry data sets. Journal of Chemometrics 2007, 20 (8?10), 325-340.
23. Lopatka, M.; Vivo-Truyols, G.; Sjerps, M. J., Probabilistic peak detection for first-order chromatographic data. Analytica Chimica Acta 2014, 817, 9-16.
24. Vivo-Truyols, G., Bayesian Approach for Peak Detection in Two-Dimensional Chromatography. Analytical Chemistry 2012, 84 (6), 2622-2630.
25. Stevenson, P. G.; Gritti, F.; Guiochon, G., Automated methods for the location of the boundaries of chromatographic peaks. Journal of Chromatography A 2011, 1218 (45), 8255-8263.
26. Phillips, M. L.; White, R. L., Dependence of Chromatogram Peak Areas Obtained by Curve-Fitting on the Choice of Peak Shape Function. Journal of Chromatographic Science 1997, 35 (2), 75-81.
27. Caballero, R. D.; Garc??a-Alvarez-Coque, M. C.; Baeza-Baeza, J. J., Parabolic-Lorentzian modified Gaussian model for describing and deconvolving chromatographic peaks. Journal of Chromatography A 2002, 954 (1), 59-76.
28. Baeza-Baeza, J. J.; Garc??a-Alvarez-Coque, M. C., Prediction of peak shape as a function of retention in reversed-phase liquid chromatography. Journal of Chromatography A 2004, 1022 (1), 17-24.
29. Yu, T.; Peng, H., Quantification and deconvolution of asymmetric LC-MS peaks using the bi-Gaussian mixture model and statistical model selection. BMC Bioinformatics 2010, 11 (1), 559.
30. Wei, X.; Shi, X.; Kim, S.; Zhang, L.; Patrick, J. S.; Binkley, J.; McClain, C.; Zhang, X., Data Preprocessing Method for Liquid Chromatography–Mass Spectrometry Based Metabolomics. Analytical Chemistry 2012, 84 (18), 7963-7971.
31. Abdehagh, N.; Bagheri, M.; Tezel, F. H.; Thibault, J., Improved Acetone-Butanol-Ethanol (ABE) Solution Analysis Using HPLC: Chromatograph Spectrum Deconvolution Using Asymmetric Gaussian Fit. American Journal of Analytical Chemistry 2014, Vol.05No.16, 12.
32. Wei, X.; Shi, X.; Kim, S.; Patrick, J. S.; Binkley, J.; Kong, M.; McClain, C.; Zhang, X., Data Dependent Peak Model Based Spectrum Deconvolution for Analysis of High Resolution LC-MS Data. Analytical Chemistry 2014, 86 (4), 2156-2165.
33. Zabell, A. P. R.; Foxworthy, T.; Eaton, K. N.; Julian, R. K., Diagnostic application of the exponentially modified Gaussian model for peak quality and quantitation in high-throughput liquid chromatography–tandem mass spectrometry. Journal of Chromatography A 2014, 1369, 92-97.
34. Liu, H.; Pang, Z.; Fan, G., Translation Modification Iteration for Resolution and Quantification of Overlapping Chromatographic Peaks. Chromatographia 2016, 79 (21), 1543-1552.
35. Peris?Diaz Manuel, D.; Alcoriza?Balaguer Maria, I.; Garcia?Canaveras Juan, C.; Santonja, F.; Sentandreu, E.; Lahoz, A., RpeakChrom: Novel R package for the automated characterization and optimization of column efficiency in high?performance liquid chromatography analysis. ELECTROPHORESIS 2017, 38 (22-23), 2985-2995.
36. Florian Obersteiner1, H. B., Timo Keber1, Simon O′Doherty3, and Andreas Engel1, A versatile, refrigerant- and cryogen-free cryofocusing–thermodesorption unit for preconcentration of traces gases in air. Atmos. Meas. Tech. 2016, 9, 5265–5279.
37. M. Farooq Wahab1, D. C. P., and Daniel W. Armstrong1, 1University of Texas at Arlington, Texas, USA, 2AbbVie; Inc., N. C., Illinois, USA, Peak Shapes and Their Measurements: The Need and the Concept Behind Total Peak Shape Analysis. LC‧GC Europe 2017, 670.
38. Wahab, M. F.; Patel, D. C.; Wimalasinghe, R. M.; Armstrong, D. W., Fundamental and Practical Insights on the Packing of Modern High-Efficiency Analytical and Capillary Columns. Analytical Chemistry 2017, 89 (16), 8177-8191.
39. Bicking, M. K. L.; ACCTA, I., St. Paul, Minnesota, Integration Errors in Chromatographic Analysis, Part I: Peaks of Approximately Equal Size. LCGC NORTH AMERICA 2006, 24.
40. Bicking, M. K. L.; ACCTA, I., St. Paul, Minnesota, Integration Errors in Chromatographic Analysis, Part II: Large Peak Size Ratios. LCGC NORTH AMERICA 2006, 24.
41. Peters, S.; Vivo-Truyols, G.; Marriott, P. J.; Schoenmakers, P. J., Development of an algorithm for peak detection in comprehensive two-dimensional chromatography. Journal of Chromatography A 2007, 1156 (1), 14-24.
42. Kim, S.; Ouyang, M.; Jeong, J.; Shen, C.; Zhang, X., A NEW METHOD OF PEAK DETECTION FOR ANALYSIS OF COMPREHENSIVE TWO-DIMENSIONAL GAS CHROMATOGRAPHY MASS SPECTROMETRY DATA. The annals of applied statistics 2014, 8 (2), 1209-1231.
43. van Stee, L. L. P.; Brinkman, U. A. T., Peak detection methods for GC?×?GC: An overview. TrAC Trends in Analytical Chemistry 2016, 83, 1-13.
44. Tomasi, G.; van den Berg, F.; Andersson, C., Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data. Journal of Chemometrics 2004, 18 (5), 231-241.
45. Bork, C.; Ng, K.; Liu, Y.; Yee, A.; Pohlscheidt, M., Chromatographic peak alignment using derivative dynamic time warping. Biotechnology Progress 2013, 29 (2), 394-402.
46. Zheng, Q.-X.; Fu, H.-Y.; Li, H.-D.; Wang, B.; Peng, C.-H.; Wang, S.; Cai, J.-L.; Liu, S.-F.; Zhang, X.-B.; Yu, Y.-J., Automatic time-shift alignment method for chromatographic data analysis. Scientific Reports 2017, 7 (1), 256.
47. Anthony Lukindo, M. I. C. A., Canada, LabVIEW QUEUED STATE MACHINE PRODUCER-CONSUMER ARCHITECTURE. Mezintel 2007.
48. Technologies, A., A Guide to Interpreting Detector Specifications for Gas Chromatographs. Technical Note 2005.
49. ASTM International, B. H. D., PO Box C700, West Conshohocken, PA 19428-2959. United States, Designation: E594 -96 : Standard Practice for Testing Flame Ionization Detectors Used in Gas or Supercritical Fluid Chromatography. 2011.
50. ASTM International, B. H. D., PO Box C700, West Conshohocken, PA 19428-2959. United States, Designation: E685 -96 : Standard Practice for Testing Fixed-Wavelength Photometric Detectors Used in Liquid Chromatography. 2013.
51. Agilent, Agilent OpenLAB Data Analysis : Reference Guide. 2012.
52. SHIMADZU, LabSolutions : Data Acquisition $ Processing Theory Guide 2012.
53. Jensen, F., Introduction to Computational Chemistryt (Second Edition). 2007.
指導教授 王家麟(Jia-Lin Wang) 審核日期 2018-7-26
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明