摘要
本论文用化学计量学的方法，建立了对大气环境中有毒易挥发有机化合物(volatile organic compounds，VOCs)的遥感傅立叶变换红外光谱(Fourier transform infrared，FTIR)谱图解析方法，文中所建立的方法，更好地发挥了遥感分析的快速、准确、实时等优势。同时，化学计量学方法在具体问题的解决中，也得到发展。由于遥感FTIR谱图存在信号噪声大、未知干扰、背景信号波动大等特点，本文从方法的普适性和稳健性角度，提出了遥感FTIR谱图的解析的方法。建立了基于偏最小二乘法(partial least squares，PLS)的谱图定量分析方法：正交信号处理(orthogonal signal
correction，OSC)方法较好地对干扰和信号进行分离，得到了一种稳健和简单化的模型；由改进PLS线性内部关系出发，建立了对5组分VOCs体系进行分析的多项式偏最小二乘(polynomial partial least squares，PPLS)的方法；创新性地引入模型传递的思路，通过选择方法以及方法的优化，用少的潜变量建立分析模型并实现了EPA数据对遥感数据的分析；用PLS方法建立对VOCs的模式识别方法，结合实验设计手段实现对VOCs定性和定量同时分析；本研究初步建立了被动式遥感FTIR谱图分析技术，创新性地利用平行因子分析法(parallel factor analysis，PARAFAC)实现了在有干扰情况下，被动式遥感FTIR谱图的定性和定量的同时分析。本文取得的基本成果总结如下：
1．基于偏最小二乘法的遥感FTIR谱图解析方法
本研究根据遥感FTIR信号特点，从方法的普适性和稳健性角度出发，建立了改进PLS的谱图分析技术。对于遥感FTIR数据，由于噪声的加入，使得PLS模型的潜变量变大，噪声数据不能同真实数据相分离，OSC-PLS有效改善了对遥感FTIR谱图的解析能力，建模潜变量个数的减少，对更复杂的体系也同样具有较好的预测性能。相对于PLS，PPLS利用非线性的内部关系，对VOCs混合物含量的预测准确度有了提高，显示出很好的处理非线性数据的能力。遗传算法(genetic algorithm，GA)可以有效选取PLS建模变量，使模型简单化的同时提高预测准确度。但是，GA-PLS对遥感数据的预测仅限于用遥感数据建立校正模型的情况。经过PLS对数据压缩，人工神经网络(artificial neural network，ANN)输入数据变小，计算精度提高。既改善了ANN模型对训练集的要求，又满足了遥感FTIR实时分析的要求。
Techniques were set up in the dissertation with the aid of chemometrics, which were used for interpretation on remote sensing FTIR spectrum of VOCs (volatile organic compounds) in the atmosphere. These especially strengthen the advantage of remote sensing FTIR in quick, accurate, real-time and simultaneous determination of multi-component analysis. Since the remote sensing FTIR spectrum containing much noise, unknown interferents and background shift, strategies were put forward in the viewpoint of model robust and popularity. Based on the multivariate calibration method PLS (partial least squares), new signal correction method OSC (orthogonal signal correction) was applied to the filtration of noise or other unrelated parts and good performance was yielded in model simplicity and robust. Furthermore, PLS were improved in the direction of inner relation with the use of PPLS (polynomial partial least squares) model and were employed in the five-component mixture successfully. Calibration transfer was applied innovatively in the analysis of the remote sensing FTIR spectrum. With the optimization of this method, remote sensing FTIR spectra were analyzed directly with EPA spectra. PLS was also used for the pattern recognition of VOCs, and qualitative and quantitative analysis were achieved simultaneously with the help of experiment design. Primary interpretation of passive remote sensing FTIR spectrum was carried out. The innovation-application of PARAFAC (parallel factor analysis) was utilized for the analysis of passive remote sensing FTIR spectrum and qualitative and quantitative analyses were realized. The main conclusions were achieved as following:
1. Interpretation on Remote Sensing FTIR Spectrum Based on PLS
According to the signal characteristics of remote sensing FTIR, PLS was improved in the viewpoint of model popularity and robust. As for the remote sensing FTIR data, noise in the data enlarged the LVs (latent variables) of PLS and made it difficult for the separation of noise and information. OSC-PLS modified this situation in prediction accuracy and fewer latent variables and efficient filtration, which was validated by the complex system. Compared with PLS, the inner relation of PPLS was nonlinear, which was used successfully for the prediction of five-component system. Variables of PLS was optimized by GA (genetic algorithm) and then made the model simple and accuracy. However, this method was only suitable for the prediction of remote sensing data with remote sensing data itself. With the data compressed by PLS, the size of ANN (artificial neural network) input was smaller and prediction accuracy
引文
1. Davidson C E. Genetic Algorithm for Data Mining and Multivariate Data Analysis. Doctorate Dissertation. Clarson University, 2003
2. S Wold. Chemometd-Kemi Och Tillampad Matematik. Year-Book of Swedish Natural Science Research Council, Stockholm, 1974: 200-206
3. Kowalski B R. Chemometrics. Anal. Chem., 1980, 52: 11R-112R
4. Lavine B, Workman J. J. Chemometrics. Anal. Chem., 2004, 76: 3365-3372
5. Lavine B, Workman J. J. Chemometrics. Anal. Chem., 2002, 74: 2763-2770
6. Lavine B. Chemometrics. Anal. Chem., 2000, 72: 91R-97R
7. Jurs P C, Bakken G A, Mcclelland H E. Computation Methods for the Analysis of Chemical Sensor Army Data from Volatile Analytes. Chem. Review., 2000, 100: 2649-2678
8．Li Yan(李燕)．Temporally and Spatially Extension of Analytical Chemistry(分析化学在时空上的延伸)．Doctorate Dissertation(博士论文)．Nanjing(南京)：Nanjing University of Science and Technology(南京理工大学)，2003
9. Sang W C., Elaine B M, Julian A M. Fault Detection Based on a Maximum-Likelihood Principal Component Analysis (PCA) Mixture. Ind Eng. Chem. Res., 2005, 44: 2316-2327
10. Andre M K P, Marcia M C F. Nondestructive Determination of Solids and Carotenoids in Tomato Products by Near-Infrared Spectroscopy and Multivariate Calibration. Anal Chem., 2005, 77: 2505-2511
11. Helene H N, Bernard A P, Isak S P, Marena M, Florian F B. Principal Component Analysis Applied to Fourier Transform Infrared Spectroscopy for the Design of Calibration Sets for Glycerol Prediction Models in Wine and for the Detection and Classification of Outlier Samples. J. Agric. Food Chem., 2004, 52: 3726-3735
12. Khajehsharifi H, Mousavi M F, Ghasemi J, Shamsipur M. Kinetic Spectrophotometric Method for Simultaneous Determination of Selenium and Tellurium Using Partial Least Squares Calibration. Anal. Chim. Acta, 2004, 512: 369-373
13. Gourvenec S., Massart D. L. Orthogonal Projection Approach (OPA) and Related Methods in Process Monitoring. Anal. Bioanal. Chem., 2004, 380: 373-375
14. Huang Xiaohong, Pan Wei, Park Soon, Han Xinqiang, Miller L W, Hall J. Modeling the Relationship between LVAD Support Time and Gene Expression Changes in the Human Heart by Penalized Partial Least Squares. Bioinformatics., 2004,20: 888-894
15. Thissena U, Pepersb M, Melssena W J, Buydens L M C. Comparing Support Vector Machines to PLS for Spectral Regression Applications. Chemom. Intell. Lab. Syst., 2004, 73: 169-179
16. Lia Baibing, Morris A J, Martin E B. Generalized Partial Least Squares Regression Based on the Penalized Minimum Norm Projection. Chemom. Intell. Lab. Syst., 2004, 72: 21-26
17. Nord L I, Vaag P, Duus J. Quantification of Organic and Amino Acids in Beer by 1H NMR Spectroscopy. Anal. Chem., 2004, 76: 4790-4798
18. Sabina B, Hans F M, Huub C J, Age K S. Constrained Least Squares Methods for Estimating Reaction Rate Constants from Spectroscopic Data. J. Chemom., 2002,16: 28-40
19. Wold S, Johan T., Anders B., Henrik A. Some Recent Developments in PLS Modeling. Chemom. Intell. Lab. Syst., 2001, 58: 131-150
20. Yan B., Yan H B. Combination of Single Bead FTIR and Chemometrics in Combinatorial Chemistry: Application of the Multivariate Calibration Method in Monitoring Solid-Phase Organic Synthesis. J. Comb. Chem., 2001, 3: 78-84
21. Estienne F, Massart D L. Mutlivariate Calibration with Raman Data Using Fast Principal Component Regression and Partial Least Squares Methods. Anal. Chim. Acta, 2001,450:123-129
22. Eustaquioa A, Blancoa M, Jeeb R D, Moffatb A C. Determination of Paracetamol in Intact Tablets by Use of Near Infrared Transmittance Spectroscopy. Anal. Chim. Acta, 1999, 383: 283-290
23. Zhou X J, Hines P A, White K C, Borer M W. Gas Chromatography as A Reference Method for Moisture Determination by Near-Infrared Spectroscopy. Anal.Chem., 1998, 70:390-394
24. Mattu M J, Small G W. Determination of Glucose in A Biological Matrix by Multivariate Analysis of Multiple Band-Pass-Filtered Fourier Transform Near-Infrared Interferograms. Anal. Chem., 1997, 69: 4695-4702
25. Hwang D, Stephanopoulos G, Chan Christina. Inverse Modeling Using Multi-Block PLS to Determine the Environmental Conditions that Provide Optimal Cellular Function. Bioinformatics, 2004, 20: 487-499
26. Henshaw J M, Burgess L W, Booksh K S., Kowalski B R. Multicomponent Determination of Chlorinated Hydrocarbons Using a Reaction-Based Chemical Sensor. 1. Multivariate Calibration of Fujiwara Reaction Products. Anal. Chem., 1994, 66: 3328-3336
27. Tauler R, Smilde A K, Henshaw J M, Burgess L W, Kowalski B R. Multicomponent Determination of Chlorinated Hydrocarbons Using a Reaction-Based Chemical Sensor. 2. Chemical Speciation Using Multivariate Curve Resolution. Anal. Chem., 1994, 66: 3337-3344
28. Smilde A K, Tauler R, Henshaw J M, Burgess L W, Kowalski B R. Multicomponent Determination of Chlorinated Hydrocarbons Using a Reaction-Based Chemical Sensor. 3. Medium-Rank Second-Order Calibration with Restricted Tucker Models. Anal Chem., 1994, 66: 3345-3351
29. Geladi P, Kowalski B R. Partial Least-Squares Regression: A Tutorial. Anal, Chim. Acta, 1986, 185: 1-17
30. Gersende F, Sophie L L. Classification Using Partial Least Squares with Penalized Logistic Regression. Bioinformatics. 2005, 21: 1104-1111
31. Matthew B, William R. Partial Least Squares for Discrimination. J. Chemom. 2003, 17: 166-173
32. Danh V N, David M R. Tumor Classification by Partial Least Squares Using Microarray Gene Expression Data. Bioinformatics., 2002, 18: 39-50
33. Iz-Abajo M, Lez-Saa Iz J, Pizarro C. Classification of Wine and Alcohol Vinegar Samples Based on Near-Infrared Spectroscopy. Feasibility Study on the Detection of Adulterated Vinegar Samples. J. Agric. Food Chem., 2004, 52: 7711-7719
34. Fang J, Beattie D S. Short Communication Identification of A Gene Encoding A 54 Kda Alternative NADH Dehydrogenase in Trypanosoma Brucei. Molecular & Biochem. Parasitology, 2003, 127: 73-77
35. Muik B, Lendl B, Molina-Diaaz A, Ortega-Calderoa D. Discrimination of Olives According to Fruit Quality Using Fourier Transform Raman Spectroscopy and Pattern Recognition Techniques. J. Agric. Food Chem., 2004, 52, 6055-6060
36. Vaid T P, Burl M C, Lewis N S. Comparison of the Performance of Different Discriminant Algorithms in Analyte Discrimination Tasks Using an Array of Carbon Black-Polymer Composite Vapor Detectors. Anal. Chem., 2001, 73: 321-331
37. Barkó G, Abonyi J, Hlavay J. Application of Fuzzy Clustering and Piezoelectric Chemical Sensor Array for Investigation on Organic Compounds. Anal. Chim. Acta, 1999, 398: 219-226
38. Linusson A, Wold S, Norden B. Fuzzy Clustering of 627 Alcohols, Guided by A Strategy for Cluster Analysis of Chemical Compounds for Combinatorial Chemistry. Chemom. Intell. Lab. Syst., 1998, 44: 213-227
39. Sorich M J, Miners J O, McKinnon R A, Winkler D A, Burden F R, Smith P A. Comparison of Linear and Nonlinear Classification Algorithms for the Prediction of Drug and Chemical Metabolism by Human UDP-Glucuronosyltransferase Isoforms. J. Chem. Inf. Comput. Sci., 2003, 43: 2019-2024
40. Barker M, Partial Least Squares for Discrimination. Doctorate Dissertation. University Of Kentucky, 2000.
41. Li T H, Mei H, Cong P H. Combining Nonlinear PLS with the Numeric Genetic Algorithm for QSAR. Chemom. Intell. Lab. Syst., 1999, 45: 177-184
42. Benigni R. Structure-Activity Relationship Studies of Chemical Mutagens and Carcinogens: Mechanistic Investigations and Prediction Approaches. Chem. Review., 2005, 105: 1767-1800
43. Litina D H, Garg R, Hansch C. Comparative Quantitative Structure-Activity Relationship Studies (QSAR) on Non-Benzodiazepine Compounds Binding to Benzodiazepine Receptor (BzR). Chem. Review., 2004, 104: 3751-3793
44．Liming Zhang(张黎明)，Lin Zhang(张琳)，Yah Li(李燕)，Xiaofei Wang(王晓斐)，Bingping Liu(刘丙萍)，Junde Wang(王俊德)．Advances In Passive OP-FTIR Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2005，25：1614-1617
45．Liming Zhang(张黎明)，Lin Zhang(张琳)，Yan Li(李燕)，Xiaofei Wang(王晓斐)，Bingping Liu(刘丙萍)，Junde Wang(王俊德)．The calibration of instrument response function during passive FTIR measurement．Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2006，26：45-47
46．Zhang Liming，Zhang Lin，Li Yan，Liu Bingping，Wang Junde．The Application of High Time Resolved Passive FTIR for Combustion Property Study of Solid Propellant Propellants, Explosives, Pyrotechnics, 2006, 42: in press
47. Yibo Ren, Yan Li, Junde Wang, Xiaofei Wang, Bingping Liu, Liming Zhang, Lin Zhang. Reconstruction Of Air Contaminant Concentration Distribution In A Two-Dimensional Plane By Computed Tomography And Remote Sensing FTIR Spectroscopy. J Environmental Science & Health, 2005, A40: 571-579.
48．Wang Junde(王俊德)．The Application of Remote Sensing in FTIR(遥感技术在傅里叶变换红外光谱中的应用)，in Modem Fourier Transform Infrared Spectroscopy and its Application(Vo1.1)(近代傅里叶变换红外光谱技术及应用)Wu Jinguang Ed(吴瑾光)．Beijing(北京)：Scientifical and Technical Documents Publishing House(科学文献出版社)，1994：442-450．
49. Li Yan, Wang Junde, Zuoru Chen, Xuetie Zhou. Artificial Neural Networks for the Quantitative Analysis of Air Toxic VOCs. Anal Lett., 2001, 34: 2203-2219
50. Li Yan, Wang Junde, Yuan Weiqun. Monitoring Leaking Gases by OP-FTIR Remote Sensing. J. Environ. Sci. & Health, 2000, A35: 1673-1679
51. Lin Zhang, Liming Zhang, Yah Li, and Junde Wang. Orthogonal Signal Correction Used For Noise Elimination Of Open Path Fourier Transform Infrared Spectra. J. Environ. Sci. & Health, 2005, A40: 1690-1669
52．Lin Zhang(张琳)，Liming Zhang(张黎明)，Yah Li(李燕)，Bingping Liu(刘丙萍)，Xiaofei Wang(王晓斐)，Junde Wang(王俊德)．Application and Improvement of Partial-Least-Squares in Fourier Transform Infrared Spectroscopy(偏最小二乘法在傅里叶变换红外光谱中的应用及进展)． SPectroscopy and Spectral Analysis(光谱学与光谱分析)，2005，25：1610-1613
53．Lin Zhang(张琳)，Liming Zhang(张黎明)，Yan Li(李燕)，Bingping Liu(刘丙萍)，Lanping Hu(胡兰萍)，Junde Wang(王俊德)．Orthogonal Signal Correction Used for Calibration Transfer of Fourier Transform Infrared Spectra(正交信号校正用于傅里叶变换红外光谱的模型传递)．Chinese J．Anal．Chem，．(分析化学)，2005，33：1709-1713
54．Lin Zhang(张琳)，Liming Zhang(张黎明)，Yan Li(李燕)，Xiaofei Wang(王晓斐)，Lanping Hu(胡兰萍)，Junde Wang(王俊德)．Multi-component Analysis on FTIR Spectra of Non-linear System Using Polynomial Partial Least Squares Method．(多项式偏最小二乘法对非线性体系红外谱图的分析)．Zpectroscopy and Spectral Analysis(光谱学与光谱分析)，2006，26：in press
55．Lin Zhang(张琳)，Liming Zhang(张黎明)，Yan Li(李燕)，Lanping Hu(胡兰 萍)，Junde Wang(王俊德)．Calibration Transfer Used For The Interpretation Of RS-FITR Spectntm(模型传递用于解析遥感傅里叶变换红外谱)． Spectroscopy and 恥ectral Analysis(光谱学与光谱分析)，2006，26：in press
56. Herget W F, Brasher J D. Remote Fourier Transform Infrared Air Pollution Studies. Opt. Eng., 1980, 19: 508-514
57. Wang Junde, Wang Xuemei., Li Hongzhi, Gu Binghe. Flame Temperature Remote Measurement from Infrared Emission Line Intensities of Rotation-Vibration Band of Molecules. Spectrosc. Lett., 1990, 23: 515-526
58. Wang Junde, Huang Mei, Wang Tianshu. Simplified Method for the Rotational Temperature Determination of Carbon Monoxide from Absorption Spectra Measured at High Resolution. Spectrosc. Lett., 1995, 28: 55-67
59. Wang Tianshu, Zhu Changjiang, Wang Junde, Xu Fuming, Chen Zuoru, Luo Yunhua. Study on Spectral Characterization of Infrared Flare Material Combustion with Remote High Resolution Fourier Transform Infrared Spectrometry. Anal. Chim. Acta, 1995, 306: 249-258
60. Li Yan, Wang Junde. The Real Time Diagnostics of Combustion Characteristics of Solid Proprellant by Remote Sensing FTIR System. Instrument. Sci. Tech., 2003, 31: 33-47
61．Liming Zhang(张黎明)，Lin Zhang(张琳)，Yah Li(李燕)，Xiaofei Wang(王晓斐)，Bingping Liu(刘丙萍)，Junde Wang(王俊德)．被动式FTIR遥感掺纳米材料固体推进剂的燃烧火焰温度．Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2006，26：in press
62. Mattu M J, Small G W, Combs R J, Knapp R B. Quantitative Analysis of Sulfur Dioxide with Passive Fourier Transform Infrared Remote Sensing Interferogram Data. Applied Spectrosc., 2000, 54: 341-348
63. Francis P, Burton M R, Oppenheimer C. Remote Measurements of Volcanic Gas Compositions by Solar Occultation Spectra. Nature, 1998, 396: 567-570
64. Oppenheimer C, Francis P, Burton M R, Maciejewski A J H, Boardman L. Remote Measurement of Volcanic Gases by Fourier Transform Infrared Spectroscopy. Applied Physics B, 1998, 67: 505-515
65. Mcgee K A., Gerlach T M. Airborne Volcanic Plume Measurements Using an FTIR Spectrometer. Geophs. Res. Lett., 1998, 25: 615-618
66. Puckrin, E, Evans W F J, Adamson, T A B. Measurement of Tropospheric Ozone by Thermal Emission Spectroscopy. Atmos. Environ., 1996, 30: 563-568
67. Mattu M J, Small G W. Quantitative Analysis of Bandpass-Filtered Fourier Transform Infrared Interferograms. Anal Chem., 1995, 67: 2269-2278
68. Tarumi T, Small G W, Combsb R J, Kroutil R T. High-Pass Filters for Spectral Background Suppression in Airborne Passive Fourier Transform Infrared Spectrometry. Anal. Chim. Acta, 2004, 501: 235-247
69. Wabomba M J, Small G W. Robust Classifier For The Automated Detection Of Ammonia In Heated Plumes By Passive Fourier Transform Infrared Spectrometry. Anal Chem., 2003, 75: 2018-2026
70. Idwasi P O, Small G W, Combs R J. Multiple Filtering Strategy for the Automated Detection of Ethanol by Passive Fourier Transform Infrared Spectrometry. Appl. Spectrosc., 2001, 55: 1544-1550
71. Koehler F W, Small G W, Combs R J, Knapp R B, Kroutil R T. Calibration Transfer Algorithm for Automated Qualitative Analysis by Passive Fourier Transform Infrared Spectrometry. Anal. Chem., 2000, 72: 1690-1698
72. Hammer C L, Small G W, Combs R J, Knapp R B, Kroutil R T. Artificial Neural Networks for the Automated Detection of Trichloroethylene by Passive Fourier Transform Infrared Spectrometry. Anal Chem., 2000, 72: 680-1689
73. Bangalore A S, Small G W, Combs R J, Knapp R B, Kroutil R T. Automated Detection of Trichloroethylene by Fourier Transform Infrared Remote Sensing Measurements. Anal. Chem., 1997, 69: 118-129
74. Shaffer R E, Small G W. Comparison of Optimization Algorithms for Piecewise Linear Discriminant Analysis: Application To Fourier Transform Infrared Remote Sensing Measurements. Anal. Chim. Acta, 1996, 331: 157-175
75. Small G W, Harms A C, Kroutil R T, Ditillo J T, Loerop W R. Design of Optimized Finite Impulse Response Digital Filters for Use with Passive Fourier Transform Infrared Interferograms. Anal Chem., 1990, 62: 1768-1777
76. Small G W, Kroutil R T, Ditillo J T, Loerop W R. Detection of Atmospheric Pollutants by Direct Analysis of Passive Fourier Transform Infrared Interferograms. Anal Chem., 1988, 60: 264-269
77. Kroutil R T, Combs R J, Knapp R B, Small G W. Remote Infrared Vapor Detection of Volatile Organic Compounds. SPIE, 2089: 24-31
78. Yang H S, Griffiths P R. Application of Multilayer Feed-Forward Neural Networks to Automated Compound Identification in Low-Resolution Open-Path FT-IR Spectrometry. Anal Chem., 1999, 71: 751-761
79. Yang H S, Griffiths P R. Encoding FT-IR Spectra in a Hopfield Network and Its Application to Compound Identification in Open-Path FT-IR Measurements Anal. Chem., 1999, 71: 3356-3364
80. Schneidera M, Blumenstocka T, Hasea F, Cuevasb E, Redondasb A, Sanchob J M. Ozone Profiles and Total Column Amounts Derived At Izan, Tenerife Island, from FTIR Solar Absorption Spectra, and Its Validation by an Intercomparison to ECC-Sonde and Brewer Spectrometer Measurements. J. Quant. Spectrosc. Radi. Transfer. 2005,91: 245-274
81. Schafera K, Jahna C, Sturmb P. Aircraft Emission Measurements by Remote Sensing Methodologies At Airports. Atmosph. Environ., 2003,37:5261-5271
82. Jiang J H, Berry R J, Siesler H W, Ozaki Y. Wavelength Interval Selection in Multicomponent Spectral Analysis by Moving Window Partial Least-Squares Regression with Applications to Mid-Infrared and Near-Infrared Spectroscopic Data. Anal. Chem., 2002, 74: 3555-3565
83. Artand B H, Griffiths P R. Effect of Resolution on Quantification in Open-Path Fourier Transform Infrared Spectrometry under Conditions of Low Detector Noise. 1. Classical Least Squares Regression. Environ. Sci. Technol., 2000, 34: 1337-1345
84. Hart B K, Berry R J, Griffiths P R. Effects of Resolution on Quantification in Open-Path Fourier Transform Infrared Spectrometry under Conditions of Low Detector Noise. 2. Partial Least Squares Regression. Environ. Sci. Technol., 2000,34: 1346-1351
85. Hashmonay R, Natschke D F, Wagoner K. Field Evaluation of a Method for Estimating Gaseous Fluxes from Area Sources Using Open-Path Fourier Transform Infrared. Environ. Sci. Technol., 2001, 35: 2309-2313
86. Bradley K S, Brooks K, Hubbard L, Popp P J, Stedman D H. Motor Vehicle Fleet Emissions by OP-FTIR. Environ. Sci. Technol., 2000, 34: 897-899
87. Harig R, Matza G, Ruscha P, Gerharda J, Schaferb K, Jahnb C, Schwenglerc P, Beild A. Remote Detection of Methane by Infrared Spectrometry for Airborne Pipeline Surveillance: First Results of Ground-Based Measurements. Proc. of SPIE, 5235: 435-446
88. Schafer K, Jahna C, Sturmb P, Lechnerb B, Bacherb M, Gostomczykc A., Kabarowskac B., Zalewskic L., Dahld G. Comparison of Remote Sensing Techniques for Measurements of Aircraft Emissions Indices at Airports. Proc. of SPIE, 5235: 425-434
89. Leardi R. Genetic Algorithm in Chemometrics and Chemistry: a Review. J. Chemom., 2001, 15: 559-569
90. Lin Wei-Qi, Jiang Jian-Hui, Shen Qi, Shen Guo-Li, Yu Ru-Qin. Optimized Block-wise Variable Combination by Particle Swarm Optimization for Partial Least Squares Modeling in Quantitative Structure-Activity Relationship Studies. J. Chem. Inf. Model. 2005, 45: 486-493
91. Fernando A I, Jose M G, Salvador G. Selection of Calibration Set Samples in Determination of Olive Oil Acidity by Partial Least Squares-Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy. Anal. Chim. Acta, 2003, 489: 59-64
92. Emilio L A, Garrigues S, Miguel G. Simultaneous Vapor Phase Fourier Transform Infrared Spectrometric Determination of Butyl Acetate, Toluene and Methyl Ethyl Ketone in Paint Solvents. Analyst, 1998, 123: 1247-1252
93. Perez-Ponce A, Rambla F J, Garrigues J M. Partial Least-Squares-Fourier Tansform Infrared Spectrometric Determination of Methanol and Ethanol by Vapor-Phase Generation. Analyst, 1998, 123: 1253-1258
94. Haaland D M, Robinson M R, Koepp G W. Reagents Near-Infrared Determination of Glucose in Whole Blood Using Multivariate Calibration. Appl. Spectrosc., 1992, 46: 1575-1578
95. Arnold M A, Small G W. Noninvasive Glucose Sensing. Anal Chem., 2005, 77: 5429-5439
96. Zhang L, Small G W, Arnold M A. Multivariate Calibration Standardization across Instruments for the Determination of Glucose by Fourier Transform Near-Infrared Spectrometry. Anal Chem., 2003, 75: 5905-5915
97. Zhang L, Small G W, Arnold M A. Calibration Standardization Algorithm for Partial Least-Squares Regression: Application to the Determination of Physiological Levels of Glucose by Near-Infrared Spectroscopy. Anal Chem., 2002, 74: 4097-4108
98. Wabomba M J, Small G W, Arnold M A. Evaluation of Selectivity and Robustness of Near-Infrared Glucose Measurements Based on Short-Scan Fourier Transform Infrared Interferograms. Anal Chim. Acta, 2003, 490: 325-340
99. Shaffer R E, Small G W, Arnold M A. Genetic Algorithm-Based Protocol for Coupling Digital Filtering and Partial Least-Squares Regression: Application to the Near-Infrared Analysis of Glucose in Biological Matrices. Anal. Chem., 1996, 68: 2663-2675
100. Ding Q, Small G W, Arnold M A. Genetic Algorithm-Based Wavelength Selection for the Near-Infrared Determination of Glucose in Biological Matrixes: Initialization Strategies and Effects of Spectral Resolution. Anal. Chem., 1998, 70: 4472-4479
101. Seung Y C, Jaebum C, Hoeil C. Feasibility of Fourier Transform Infrared Spectroscopy for Monitoring Heavy Water Concentration in Pressurized Heavy Water Reactor. Vib. Spectrosc., 2003, 31: 251-256
102. Emma S H, Anthon D W, Stephen J H. Quantitative Fourier Transform Infrared Spectroscopy of Binary Mixture of Fatty Acid Ester Using Partial Least Squares Regression. Anal. Chim. Acta, 1997, 337:191-199
103. Isaksson T, Kowalski B. Piece-Wise Multiplicative Scatter Correction Applied to Near-Infrared Diffuse Transmittance Data from Meat Products. Appl. Spectrosc., 1993, 47: 702-709
104. Wold S, Antti H, Lindgren F. Orthogonal Signal Correction of Near-Infrared Spectra. Chemom. Intell. Lab. Syst., 1998,44: 175-185
105. Wold S, Sjostrom M. Chemometrics, Present and Future Success. Chemom. Intell. Lab. Syst., 1998, 44: 3-14
106. Pizarro C, Esteban D I, Nistal J A, Gonzalez M S. Influence of Data Pre-Processing on the Quantitative Determination of the Ash Content and Lipids in Roasted Coffee by Near Infrared Spectra. Anal. Chim. Acta, 2004, 509: 217-227
107. Thomas E V, Haaland D M. Comparison of Multivariate Calibration Methods for Quantitative Spectral Analysis. Anal. Chem., 1990, 62: 1091-1097
108. Thomas E V. Advantage Of Partial Least Squares Regressopm. Anal. Chem., 1994, 66: 795A-797A
109. Spiegelman C H, Mcshane M J, Geotz M J. Theoretical Justification of Wavelength Selection in PLS Calibration: Development of A New Algorithm. Anal. Chem., 1998, 70: 35-44
110. Norgaard L, Saudland A, Wagner J, Nielsen J P.New Approach For Variables Selection Of PLS. Appl Spectrosc, 2000, 54: 413-419
111. Du Y P, Liang Y Z, Jiang J H, Berry R J, Ozaki Y. Spectral Regions Selection to Improve Prediction Ability of PLS Models by Changeable Size Moving Window Partial Least Squares and Searching Combination Moving Window Partial Least Squares. Anal Chim. Acta, 2004, 501: 183-191
112. Madan J, Dwivedi A K, Singh S. Estimation of Antitubercular Drugs Combination in Pharmaceutical Formulations Using Multivariate Calibration. Anal. Chim. Acta, 2005, 538: 345-353
113. Pavan M, Mauri A, Todeschini R. Total Ranking Models by the Genetic Algorithm Variable Subset Selection(GA-VSS) Approach for Environmental Priority Settings. Anal Bioanal. Chem., 2004, 380: 430-444
114. Abdollahi H, Bagheri L. Simultaneous Spectrophotometric Determination of Vitamin K3 and 1,4-Naphthoquinone after Cloud Point Extraction by Using Genetic Algorithm Based Wavelength Selection-Partial Least Squares Regression. Anal. Chim. Acta, 2004, 514: 211-218
115. Liu H X, Zhang R S, Yao X J, Liu M C, Hu Z D, Fan B T. Prediction of the Isoelectric Point of an Amino Acid Based on GA-PLS and SVMs. J. Chem. Inf. Comput. Sci., 2004, 44: 161-167
116. Gourvénec S, Capron X, Massart D L. Genetic Algorithms (GA) Applied to the Orthogonal Projection Approach (OPA) for Variable Selection. Anal. Chim. Acta, 2004, 519: 11-21
117. Goicoechea H C, Olivieri A C. Wavelength Selection for Multivariate Calibration Using a Genetic Algorithm: A Novel Initialization Strategy. J. Chem. Inf. Comput. Sci., 2002, 42: 1146-1153
118. Barros A S, Rutledge D N. Genetic Algorithm Applied to the Selection of Principal Components. Chemom. IntelL Lab. Syst., 1998, 40: 65-81
119. Bangalore A S., Shaffer R E., Small G W. Genetic Algorithm-Based Method for Selecting Wavelengths and Model Size for Use with Partial Least-Squares Regression: Application to Near-Infrared Spectroscopy. Anal. Chem., 1996, 68: 4200-4212
120. Lavine B K., Davidson C E, Moores A J. Innovative Genetic Algorithms for Chemoinformatics. Chemom. Intell. Lab. Syst., 2002, 60: 161-171
121. Keun H C, Ebbels T M, Antti H, Bollard M E. Improved Analysis of Multivariate Data by Variable Stability Scaling: Application to NMR-Based Metabolic Profiling. Anal. Chim. Acta, 2003, 490: 265-276
122. Sato H, Kiguchi M, Kawaguchi F, Maki A. Practicality of Wavelength Selection to Improve Signal-To-Noise Ratio in Near-Infrared Spectroscopy. NeuroImage, 2004, 21: 1554-1562
123. Xu L., Schechter I. Wavelength Selection for Simultaneous Spectroscopic Analysis. Experimental and Theoretical Study. Anal. Chem., 1996, 68: 2392-2400
124. Kalivas J H, Roberts N, Sutter J M. Global Optimization by Simulated Annealing with Wavelength Selection for Ultraviolet-Visible Spectrophotometry. Anal. Chem., 1989,67: 2024-2030
125. Liu Shiqing, Wang Weiwen. A Study on the Applicability on Multicomponent Calibration Methods in Chemometrics. Chemom. Intell. Lab. Syst., 1999,45:131-133
126. Zhang M H., Xu Q S., Massart D L. Averaged and Weighted Average Partial Least Squares. Anal. Chim. Acta, 2004, 504: 279-289
127. Douglas N R, Antonio B, Ivonne D. Polish-Smoothed Partial Least-Square Regression. Anal. Chim. Acta, 2001, 446: 281-286
128. Ruscio D D. A Weighted View on the Partial Least-Squares Algorithm. Automatica, 2000, 36: 831-850
129. Bertran E, Blanco M, Maspoch S, Ortiz M C, Sanchez M S, Sarabia L A. Handling Intrinsic Non-Linearity in Near-Infrared Reflectance Spectroscopy. Chemom. Intell. Lab. Syst., 1999,49: 215-224
130. Filhoa D, Galva R K H, Araujoa M C U, Silvaa E C, Saldanhaa TCB, Jose E. A Strategy for Selecting Calibration Samples for Multivariate Modeling Heronides Adonias. Chemom. Intell. Lab. Syst., 2004, 72: 83-91
131. Devos O, Fanget B, Saber A, Paturel L, Naffrechoux E, Jarosz J. Use of a Plackett-Burman Design with Multivariate Calibration for the Analysis of Polycyclic Aromatic Hydrocarbons in Micellar Media by Synchronous Fluorescence. Anal. Chem., 2002, 74: 678-683
132. Filhoa H A, Galvaob R K. A Strategy For Selecting Calibration Samples For Multivariate Modeling. Chemom. Intell. Lab.Syst., 2004, 72: 83-91
133. Ragno G, Ioele G, Risoli A. Multivariate Calibration Techniques Applied to the Spectrophotometric Analysis of One-to-four Component Systems. Anal. Chim. Acta, 2004, 512: 173-180
134. Munoz J. A., Brereton R. G. Partial Factorial Designs for Multivariate Calibration: Extension to Seven Levels and Comparison of Strategy. Chemom. Intell. Lab. Syst., 1998, 43: 89-105
135. Eriksson L, Johansson E, Wikstrom C. Mixture Design-Design Generation, PLS Analysis, and Model Usage. Chemom. Intell. Lab. Syst., 1998, 43: 1-24
136. Zhang M H, Xu Q S, Massart D L. Boosting Partial Least Squares. Anal. Chem., 2006, 66: 3328-3336
137. Ryken M A, Visser J P. On-Line Detection and Identification of Intererence in Multivariate Predictions of Organic Gases Using FT-IR Spectra. Anal Chem,, 1995, 67: 2170-2179
138. Dieterle F, Busche S, Gauglitz G. Different Approaches to Multivariate Calibration of Nonlinear Sensor Data. Anal Bioanal Chem., 2004, 380: 383-396
139. Baffi G, Martin E B, Morris A J. Non-Linear Dynamic Projection to Latent Structures Modeling. Chemom. Intell. Lab. Syst., 2000, 52: 5-22
140. Grate J W, Wise B M, Abraham M H. Method for Unknown Vapor Characterization and Classification Using a Multivariate Sorption Detector. Initial Derivation and Modeling Based on Polymer-Coated Acoustic Wave Sensor Arrays and Linear Solvation Energy Relationships. Anal. Chem., 1999, 71: 4544-4553
141. Robertsson G. Contributions to the Problem of Approximation of Non-Linear Data with Linear PLS in an Absorption Spectroscopic Context. Chemom. Intell. Lab. Syst., 1999, 47: 99-106
142. Hadjiiski L, Geladi P. A Comparison of Modeling Nonlinear Systems with Artificial Neural Networks and Partial Least Squares. Chemom. Intell. Lab. Syst., 1999, 49: 91-103
143. Dickert F L, Hayden O, Zenkel M E. Detection of Volatile Compounds with Mass-Sensitive Sensor Arrays in the Presence of Variable Ambient Humidity. Anal Chem., 1999, 71: 1338-1341
144. Domansky K, Baldwin D L, Grate J W, Hall T B., Li J, Josowicz M, Janata J. Development and Calibration of Field-Effect Transistor-Based Sensor Array for Measurement of Hydrogen and Ammonia Gas Mixtures in Humid Air. Anal. Chem., 1998, 70: 473-481
145. Nakanishia J, Farrellc J A, Schaala S. Composite Adaptive Control with Locally Weighted Statistical Learning. Neural Networks, 2005, 18: 71-90
146. Naes T, Isaksson T. Kowalski B R. Locally Weighted Regression and Scatter Correction for Near-Infrared Reflectance Data. Anal. Chem., 1990, 62: 664-673
147. Wang Z, Isaksson T, Kowalski B R. New Approach for Distance Measurement in Locally Weighted Regression. Anal. Chem., 1994, 66: 249-260
148. Centner V, Massart D L. Optimization in Locally Weighted Regression. Anal. Chem., 1998,70:4206-4211
149. Despagne F, Massart D L., Chabot P. Development of a Robust Calibration Model for Nonlinear In-Line Process Data. Anal. Chem., 2000,72: 1657-1665
150. Despagne F, Massart D L., Noord O E. Optimization of Partial-Least-Squares Calibration Models by Simulation of Instrumental Perturbations. Anal. Chem., 1997, 69: 3391-3399
151. Yang H S, Griffith P R, Tate J D. Comparison of Partial Least Squares Regression and Multi-Layer Neural Networks for Quantification of Nonlinear Systems and Application to Gas Phase Fourier Transform Infrared Spectra. Anal. Chim. Acta, 2003,489:125-132
152. Bro B. Multiway Calibration: Multilinear PLS. J. Chemom., 1996, 10: 47-62
153. Kiyoshi H, Masamoto A, Kimito F. 3D-QSAR Study of Insecticidal Neonicotinoid Compounds Based on 3-Way Partial Least Squares Model. Chemom. Intell. Lab. Syst., 1999, 47: 33-40
154. Nilsson J, De Jong S, Smilde A. K. Multiway Calibration in 3D QSAR. J. Chemom.,1997, 11: 511-524
155. Jong D S, "SIMPLS: An Alternative Approach to Partial Least Squares Regression". Chemom Intell. Lab. Syst., 1993, 18: 251-263
156. Lorber A, Faber K, Kowalski B R. Net Analyte Signal Calculation in Multivariate Calibration. Anal. Chem., 1997, 69: 1620-1626
157. Barrosa A. S., Rutledgeb D. N. Principal Components Transform-Partial Least Squares: A Novel Method to Accelerate Cross-Validation in PLS Regression. Chemom. Intell. Lab. Syst., 2004, 73: 245-255
158. Serneelsa S, Crouxb C, Espena P J V. Influence Properties of Partial Least Squares Regression. Chemom. Intell. Lab. Syst., 2004, 71: 13-20
159. Helland I S. Some Theoretical Aspects of Partial Least Squares Regression. Chemom. Intell. Lab. Syst., 2001, 58: 97-107
160. Wua W, Manne R. Fast Regression Methods in A Lanczos or PLS-1 Basis.Theory and Applications. Chemom. Intell. Lab. Syst., 2000, 51: 145-161
161. Frenich A G, Martynez V J L, Galera M M. Use of the Cross-Section Technique Linked with Multivariate Calibration Methods to Resolve Complex Pesticide Mixtures. Anal Chem., 1999, 71: 4844-4850
162. Wentzell P D, Montoto L V. Comparison of Principal Components Regression and Partial Least Squares Regression through Generic Simulations of Complex Mixtures. Chemom. Intell. Lab. Syst., 2003, 65: 257-279
163. Dupuy N, Duponchel L, Amram B, Huvenne J P, Legrand P. J. Chemom. 1994, 8: 333-347
164. Blanco M, Coello J, Iturriaga H, Maspoch S, Pages J. Calibration in Non-Linear Near Infrared Reflectance Spectroscopy: A Comparison of Several Methods. Anal. Chim. Acta, 1999, 384: 207-214
165. Eklov T, Martensson P, Lundstrom I. Selection of Variables for Interpreting Multivariate Gas Sensor Data. Anal. Chim. Acta, 1999, 381: 221-224
166. Srivastava A K. Detection of Volatile Organic Compounds (VOCs) Using SnO_2 Gas-Sensor Array and Artificial Neural Network. Sensor Actuat. B 2003, 96: 24-37
167. Sjoblom J, Svensson O, Josefson M. An Evaluation of Orthogonal Signal Correction Applied to Calibration Transfer of Near Infrared Spectra. Chemom. Intell. Lab. Syst., 1999, 44: 229-244
168. Woody N A, Feudale R N, Brown S D. Transfer of Multivariate Calibration between Four Near-Infrared Spectrometers Using Orthogonal Signal Correction. Anal. Chem., 2004, 76: 2595-2600
169. Fearn T. On orthogonal signal correction. Chemom. Intell. Lab. Syst., 2000, 50: 47-52
170. Yee G N, Coghill G G. Factor Selection Strategies for Orthogonal Signal Correction Applied to Calibration of Near-Infrared Spectra. Chemom. Intell. Lab. Syst., 2003, 67: 145-156
171. Blanco M, Coello J, Montoliu I, Romero M. A. Orthogonal Signal Correction in Near Infrared Calibration. Anal. Chim. Acta, 2001, 434: 125-132
172. Bertran E, Iturriaga H, Maspoch S, Montoliu I. Effect of Orthogonal Signal Correction on the Determination of Compounds with Very Similar Near Infrared Spectra. Anal Chim. Acta, 2001, 431: 303-311
173. Feudale R N, Tan H, Brown S D. Piecewise Orthogonal Signal Correction. Chemom. Intell. Lab. Syst., 2002, 63: 129-138
174. Andersson C A. Direct orthogonalization. Chemom. Intell. Lab. Syst. 1999, 47: 51-63
175. Goicoechea H C, Olivieri A C. A Comparison of Orthogonal Signal Correction and Net Analyte Preprocessing Methods. Theoretical and Experimental Study. Chemom. Intell. Lab. Syst., 2001, 56: 73-81
176. Andrew A, Fearn T. Transfer by Orthogonal Projection: Making Near-Infrared Calibrations Robust to Between-Instrument Variation. Chemom. Intell. Lab. Syst., 2004, 72:51-56
177. Yu H, MacGregor J F. Post processing methods (PLS-CCA): Simple Alternatives to Preprocessing Methods (OSC-PLS). Chemom. Intell Lab. Syst., 2004, 73: 199-205
178. Kasemsumrana S, Dub Y P, Maruoc K, Ozakia Y. Selective Removal of Interference Signals for Near-Infrared Spectra of Biomedical Samples by Using Region Orthogonal Signal Correction. Anal. Chim. Acta, 2004, 526: 193-202
179. Ghasemi J, Saaidpour S, Ensafi A A. Simultaneous Kinetic Spectrophotometric Determination of Periodate and Iodate Based on Their Reaction with Pyrogallol Red in Acidic Media by Chemometrics Methods. Anal. Chim. Acta, 2004,508:119-126
180. Eriksson L, Trygg J, Johansson E, Bro R, Wold S. Orthogonal Signal Correction, Wavelet Analysis, and Multivariate Calibration of Complicated Process Fluorescence Data. Anal. Chim. Acta, 2000,420: 181-195
181. Forrest S. Genetic Algorithm: Principles of Natural Selection Applied to Computation. Science, 1993,261:872-878
182. Maddox J. Genetics Helping Molecular Dynamics. Nature, 1995, 376: 209-212
183. Blank T B, Sum S T, Brown S D. Transfer of Near-Infrared Multivariate Calibrations without Standards. Anal.Chem., 1996, 68: 2987-2995
184. Wang Z, Dean T, Kowalski B R. Additive Background Correction in Multivariate Instrument Standardization. Anal. Chem., 1995, 67: 2379-2385
185. Wang Y, Veltkamp D J, Kowalski B R. Multivariate Instrument Standardization. Anal. Chem., 1991,63: 2750-2756
186. Xie Y, Hopke P K. Calibration Transfer as a Data Reconstruction Problem. Anal. Chim. Acta, 1999, 384: 193-205
187. Pavon Jose L, Sanchez M N, Cordero B M. Calibration Transfer for Solving the Signal Instability in Quantitative Headspace-Mass Spectrometry. Anal. Chem., 2003, 75: 6361-6367
188. Zhou Xuetie, Li Yan, Wang Junde, Huang Zhonghua. The Temperature Measurement of the Electrothermal Laucher Plasma by Atomic Emission Spectroscopy. IEEE Trans. Plasma Sci., 2001, 29: 360-364
189. Wulfert F, Kok W T., Smilde A K. Influence of Temperature on Vibrational Spectra and Consequences for the Predictive Ability of Multivariate Models. Anal. Chem., 1998, 70: 1761-1767
190 Wulfert F, Kok W T., Noord O E, Srnilde A K. Linear Techniques to Correct for Temperature-Induced Spectral Variation in Multivariate Calibration. Chemom. IntelL Lab. Syst., 2000, 51: 189-200
191 Wulfert F, Kok W T, Noord O E, Smilde A K. Correction of Temperature-Induced Spectral Variation by Continuous Piecewise Direct Standardization. Anal. Chem., 2000, 72: 1639-1644
192 Sanchez E, Kowalski B R. Generalized Rank Annihilation Factor Analysis. Anal. Chem., 1986, 58:r496-499
193 Bro R. PARAFAC. Tutorial and Applications. Chemom. Intell. Lab. Syst., 1997, 38: 149-171
194. Olivieri A C. Computing Sensitivity and Selectivity in Parallel Factor Analysis and Related Multiway Techniques: the Need for Further Developments in Net Analyte Signal Theory. Anal Chem., 2005, 77: 4936-4946
195. Munoz de A, Mansilla A E, Gonza D, Olivieri A C, Goicoechea H. Interference-Free Analysis Using Three-Way Fluorescence Data and the Parallel Factor Model. Determination of Fluoroquinolone Antibiotics in Human Serum. Anal. Chem., 2003, 75: 2640-2646
196. Christensen J H, Hansen A B, Mortensen J, Andersen O. Characterization and Matching of Oil Samples Using Fluorescence Spectroscopy and Parallel Factor Analysis. Anal Chem., 2005, 77: 2210-2217
197. Olivieria A., Faberb N. Standard Error of Prediction in Parallel Factor Analysis of Three-Way Data. Chemom. Intell. Lab. Syst., 2004, 70: 75-82
198. Tomasi G, Bro R. A Comparison of Algorithms for Fitting the PARAFAC Model. Comput. Stat. & Data Anal., 2006, 50: 1700-1734