支持向量机回归—近红外光谱法用于药物无损非破坏定量分析的研究
摘要
本文应用近红外光谱(NIR Spectroscopy)结合支持向量机回归(SVR),对三种不同药物的有效活性成分进行了定量分析,建立了适合于不同药物的支持向量回归模型,取得较好的成果,实现了药物的无损非破坏定量分析。研究并建立了活性成分的土霉素药品粉末,氨苄西林药品粉末和氧氟沙星药品粉末的最佳SVR模型。本文采用交叉验证均方差(RMSECV)作为交叉验证的检验指标,以相对偏差(RSE)来考察所建模型的性能和预测效果,并计算交叉验证和预测集上的相关系数(R),研究结果表明SVR方法具有一定的处理高维有限数量的非线性数据能力。鉴于药物分析领域小样本统计的课题甚多,将SVR理论应用于解决此类实际问题的研究具有一定意义。
Since the 20th century, due to the development of modern science and technology, the development of analytical chemistry experienced three great changes, so that modern analytical chemistry has developed into a subject of which instrumental analysis is the main stream. Drug analysis is an important component of analytical chemistry as well as of the drug science. New demands are proposed as the result of the development of drug science and the human pursuit of quality of drugs. In recent years, due to the rapid development of science and technology and mutual infiltration of subjects, drug analysis has developed into instrument-analysis-based modern drug analysis. The main task of Modern drug analysis is to effectively control the quality of drug raw materials and preparations in the processes of drug development, production, distribution and use by means of accurate and rapid analysis to ensure the safety and effectiveness of drug use. The advent of the Information Age, of which computer application is the main indicator, accelerates the development of analysis technology, especially the development of instrument analysis become more rapidly, which not only provides a variety of very sensitive, accurate and rapid means for the drug analysis subject, but also by getting into the body, monitors dynamically in the process of reaction. Drug analysis develops towards a more precise, fast, convenient, trace, and environmental protection way.
With the advent of the 21st century, the medical model has gone through the transition from treatment to prevention. At present, the development of pharmacy at home and abroad is very rapid, firstly, new drugs are launched, and widely used in clinical, and secondly, antibiotic medicines replacement cycle was shortened, and thirdly, the old drugs were used in new different ways. The main reason is that the basic pharmaceutical research was draw wide attention attached great importance by governments as well as the result of the close cooperation of doctors and pharmacy workers studies. Therefore, the urgent task the drug workers are facing is no longer just static conventional drug testing, but to go into the process of drug production, and to achieve real-time, in situ, and online quality control to drug raw materials, intermediates, semi-finished and finished products.
Nowadays in the quantitative analysis of drugs, it is commonly used chromatography (GC and HPLC) and spectroscopy (IR and UV) as the pharmacopoeia standard method and the Ministry of Health universal standard method. However, these methods, without exception, must dissolve samples, isolate its ingredients, and then further measure the content of the ingredients, which is time-consuming and laborious. The thesis will focus on using SVM-NIR spectrometry, for the analysis of drug quality, that is, non-destructive rapid monitoring the quality of medicines in order to achieve real-time online monitoring of drug quality.
NIR spectroscopy method is a fast and efficient online testing technology, which is used to do multi-component analysis at the same time by the multivariate calibration model. Since the 1990s NIR has developed rapidly and been the most attractive spectrometry technology, which is the integration of the spectral measurement technology and stoichiometry, known as the Giant of the analysis. One of its key steps is the establishment of multivariate calibration model on NIR spectroscopy measurement data. Typically, only when we get a large number of known samples that is well-distributed, can we gain the spectrum analysis accurately. In the practical analytical work, however, medicines and other objects are often difficult to obtain a large number of known samples, and there always exists nonlinear mapping between the spectral data and concentration of the component. Thus the study of nonlinear modeling of fewer samples is the direction and the focus of the NIR spectroscopy analysis. Recently, many researchers apply artificial neural network (ANN) method to establish the nonlinear regression model of NIR spectroscopy, under the condition of more known samples, we can get a model that can correct and anticipate the relatively smaller errors by means of ANN. However, in the field of drug analysis most of the data modeling issue is a mathematical small sample of ill-posed problem, and traditional methods ,such as linear and non-linear regression, artificial neural networks ignored this characteristic, often view them as endless samples , well posed problem , data modeling has led to the "overfitting" and poor generalization, that is the problem of better fitting for known samples but worse for the unknown samples, but may cause obstacles in practice.
The support vector machines (SVM) founded by Vapnik, performed a unique characteristics and superiority on the promotion in the machine learning, has been extensive studied and applied. And different from the conventional neural network based on the experience of risk minimization principle (ERM), SVM is based on statistical learning theory of structural risk minimization principle, which is strictly based on the theory and mathematics. The core idea of SVM is that the input space through specific function mapped to the high-dimensional feature space, constructed classification hyperplane that can maximize the interval category and decision-making rules so as to control model capacity (complexity), inhibit overfitting, and improve the performance of promoting model, by SVM it we can solve the non-linear, high-dimension, the local minimum point and other problems, and small sample studies make it a strong generalization.
SVM has four notable features:
1). with the idea of large interval to reduce the VC-dimensional (Vapnik-Chervonenkis Dimension) of classifier, to implement structural risk minimization principle, to control capacity of the promotion of classification, and to maximize the generalization of the learning ability;
2). With Mercer kernel to achieve non-linear and linear algorithm. A major feature of SVM is that nonlinear problems are mapped to linear problems of the high-dimensional space, which is replaced by a kernel function in the plot operation, thus avoiding the high-dimensional operations, ingeniously solved the nonlinear problems;
3). It is sparse, that is the coefficient of small sample (SV) can be any but zero. In the aspect of the promotion, the less support vector is, the better the promotion capacity is in terms of statistical significance. And in terms of calculation, support vector reduces the calculation of the kernel form discriminant;
4). Algorithm concerns of convex optimization, thus local optimal solution must be global optimal solution and avoids the result of multi-solutions.
The above features are not shared by other learning algorithm such as neural network learning algorithm.
In this thesis, considering the complex characteristics of pharmaceutical composition and the superiority of establishing non-linear model by means of support vector regression, we combined SVM and NIR in the quantitative analysis of its active ingredients, forecasted active ingredients of three different drugs in a quantitative way, thus established a practical forecasting model, and achieved the real non-destructive quantitative analysis. This paper applies root mean square error of cross-validation (RMSECV) to estimate cross-validation, and relative standard error (RSE) to measure the performance and the predictive abilities of the model, and it also calculates the cross-validation and the correlation coefficient (R) of the Test set.
Compared with the conventional drug test method, this method can make a quantitative analysis of drug quickly and accurately, particularly applicable to the online quality control of drugs; meanwhile in the access of quantitative analysis it did not undermine the composition of medicines, undoubtedly this method means a lot for some expensive drugs . In view of the large numbers of subjects in the statistics field of small samples of drugs, SVM in drug analysis is very promising; therefore the application of SVM theory to solve the practical problems is of great significance.
Since the 20th century, due to the development of modern science and technology, the development of analytical chemistry experienced three great changes, so that modern analytical chemistry has developed into a subject of which instrumental analysis is the main stream. Drug analysis is an important component of analytical chemistry as well as of the drug science. New demands are proposed as the result of the development of drug science and the human pursuit of quality of drugs. In recent years, due to the rapid development of science and technology and mutual infiltration of subjects, drug analysis has developed into instrument-analysis-based modern drug analysis. The main task of Modern drug analysis is to effectively control the quality of drug raw materials and preparations in the processes of drug development, production, distribution and use by means of accurate and rapid analysis to ensure the safety and effectiveness of drug use. The advent of the Information Age, of which computer application is the main indicator, accelerates the development of analysis technology, especially the development of instrument analysis become more rapidly, which not only provides a variety of very sensitive, accurate and rapid means for the drug analysis subject, but also by getting into the body, monitors dynamically in the process of reaction. Drug analysis develops towards a more precise, fast, convenient, trace, and environmental protection way.
With the advent of the 21st century, the medical model has gone through the transition from treatment to prevention. At present, the development of pharmacy at home and abroad is very rapid, firstly, new drugs are launched, and widely used in clinical, and secondly, antibiotic medicines replacement cycle was shortened, and thirdly, the old drugs were used in new different ways. The main reason is that the basic pharmaceutical research was draw wide attention attached great importance by governments as well as the result of the close cooperation of doctors and pharmacy workers studies. Therefore, the urgent task the drug workers are facing is no longer just static conventional drug testing, but to go into the process of drug production, and to achieve real-time, in situ, and online quality control to drug raw materials, intermediates, semi-finished and finished products.
Nowadays in the quantitative analysis of drugs, it is commonly used chromatography (GC and HPLC) and spectroscopy (IR and UV) as the pharmacopoeia standard method and the Ministry of Health universal standard method. However, these methods, without exception, must dissolve samples, isolate its ingredients, and then further measure the content of the ingredients, which is time-consuming and laborious. The thesis will focus on using SVM-NIR spectrometry, for the analysis of drug quality, that is, non-destructive rapid monitoring the quality of medicines in order to achieve real-time online monitoring of drug quality.
NIR spectroscopy method is a fast and efficient online testing technology, which is used to do multi-component analysis at the same time by the multivariate calibration model. Since the 1990s NIR has developed rapidly and been the most attractive spectrometry technology, which is the integration of the spectral measurement technology and stoichiometry, known as the Giant of the analysis. One of its key steps is the establishment of multivariate calibration model on NIR spectroscopy measurement data. Typically, only when we get a large number of known samples that is well-distributed, can we gain the spectrum analysis accurately. In the practical analytical work, however, medicines and other objects are often difficult to obtain a large number of known samples, and there always exists nonlinear mapping between the spectral data and concentration of the component. Thus the study of nonlinear modeling of fewer samples is the direction and the focus of the NIR spectroscopy analysis. Recently, many researchers apply artificial neural network (ANN) method to establish the nonlinear regression model of NIR spectroscopy, under the condition of more known samples, we can get a model that can correct and anticipate the relatively smaller errors by means of ANN. However, in the field of drug analysis most of the data modeling issue is a mathematical small sample of ill-posed problem, and traditional methods ,such as linear and non-linear regression, artificial neural networks ignored this characteristic, often view them as endless samples , well posed problem , data modeling has led to the "overfitting" and poor generalization, that is the problem of better fitting for known samples but worse for the unknown samples, but may cause obstacles in practice.
The support vector machines (SVM) founded by Vapnik, performed a unique characteristics and superiority on the promotion in the machine learning, has been extensive studied and applied. And different from the conventional neural network based on the experience of risk minimization principle (ERM), SVM is based on statistical learning theory of structural risk minimization principle, which is strictly based on the theory and mathematics. The core idea of SVM is that the input space through specific function mapped to the high-dimensional feature space, constructed classification hyperplane that can maximize the interval category and decision-making rules so as to control model capacity (complexity), inhibit overfitting, and improve the performance of promoting model, by SVM it we can solve the non-linear, high-dimension, the local minimum point and other problems, and small sample studies make it a strong generalization.
SVM has four notable features:
1). with the idea of large interval to reduce the VC-dimensional (Vapnik-Chervonenkis Dimension) of classifier, to implement structural risk minimization principle, to control capacity of the promotion of classification, and to maximize the generalization of the learning ability;
2). With Mercer kernel to achieve non-linear and linear algorithm. A major feature of SVM is that nonlinear problems are mapped to linear problems of the high-dimensional space, which is replaced by a kernel function in the plot operation, thus avoiding the high-dimensional operations, ingeniously solved the nonlinear problems;
3). It is sparse, that is the coefficient of small sample (SV) can be any but zero. In the aspect of the promotion, the less support vector is, the better the promotion capacity is in terms of statistical significance. And in terms of calculation, support vector reduces the calculation of the kernel form discriminant;
4). Algorithm concerns of convex optimization, thus local optimal solution must be global optimal solution and avoids the result of multi-solutions.
The above features are not shared by other learning algorithm such as neural network learning algorithm.
In this thesis, considering the complex characteristics of pharmaceutical composition and the superiority of establishing non-linear model by means of support vector regression, we combined SVM and NIR in the quantitative analysis of its active ingredients, forecasted active ingredients of three different drugs in a quantitative way, thus established a practical forecasting model, and achieved the real non-destructive quantitative analysis. This paper applies root mean square error of cross-validation (RMSECV) to estimate cross-validation, and relative standard error (RSE) to measure the performance and the predictive abilities of the model, and it also calculates the cross-validation and the correlation coefficient (R) of the Test set.
Compared with the conventional drug test method, this method can make a quantitative analysis of drug quickly and accurately, particularly applicable to the online quality control of drugs; meanwhile in the access of quantitative analysis it did not undermine the composition of medicines, undoubtedly this method means a lot for some expensive drugs . In view of the large numbers of subjects in the statistics field of small samples of drugs, SVM in drug analysis is very promising; therefore the application of SVM theory to solve the practical problems is of great significance.
引文
[1] 俞汝勤,化学计量学导论[M],长沙,湖南教育出版社.1991,3-4
[2] Words,“Chemometries:what do we mean with it, and What do we want from it?”Paper of InCINC’94; httP://www.emsl.pnl.gov:2080/docs/incinc/homepage.html
[3] Steven D.Brown,Stephen T.Sum,Frederic Despagne and Barry K.Lavine.Chemometrics.Anal.Chem.[J].1996,68:21R-61R
[4] Barry K.Lavine.Chemometrics.Anal.Chem.[J].1998,70:209R-228R
[5] Barry K.Lavine.Chemometrics.Anal.Chem.[J].2000,72:91R-97R
[6] Barry Lavine, and Jerome J.Workman, Jr. Chemometrics. Anal. Chem. [J]. 2004, 76:3365-3372
[7] 许禄,化学计量学方法[M].科学出版社,1995,1
[8] 梁逸曾, 白灰黑复杂多组分分析体系及其化学计量学算法,长沙,湖南科学技术出版社, 1996
[9] Latinen H.A., Anal.Chem., 1980, 52: 602A
[10] 中国科学院长春应用化学研究所,“现在分析化学规划”,1996
[11] 高鸿,“分析化学现状与未来”(“分析化学前沿”主题报告),北京.科学出版社,1991
[12] The Chemieal Soeiety, J. Chem. Inf. ComP. Sci., 1975,15:201
[13] Murry A., Anal. Chem., 1991,63:271A
[14] LiangYizeng, Kvalheim O.M., Manne R.,Chemom.Intell.Lab.Sys., 1993, 18:235
[15] 国家自然科学基金会,“自然科学学科发展战略调研报告”,“分析化学”, 北京, 科学出版社, 1993
[16] Howery D.G., Journal of Chem Educ.1983,60: 656
[17] Massart, D.L., Vandeginste, B.G.M., Buydens, L.M.C., de Jong, S., Lewi, P.J., Smeyers-Verbeke, J.Handbook of chemometries and qualimetries. Ainsterdam: Elsevier Scienee, 1997.
[18] 梁逸曾,俞汝勤.分析化学手册一化学计量学(第十分册),第二版.北京:化学工业出版社,2000.
[19] Wbld, S.Chemmetries; what do we mean with it, and what do we want from it? Chemom.Intell.Lab.Syst.1995, 30(l):109.
[20] Bacci M.,Picollo M.,Porcinai S.,Radicati B..Tempera-Painted Dosim -eters for Environmental Indoor Monitoring:A Spectroscopic and Chem ometric Approach. Environ.Sci.Technol[J]. 2000,34(13): 2859 -2865
[21] María Valeria Amé,María del Pilar Díaz,Daniel Alberto Wunder -lin.Occurrence of toxic cyanobacterial blooms in San Roque Reservoir (Córdoba,Argentina):A field and chemometricstudy.Environmental Toxico -logy[J].2003,18(3):192-201
[22] Cocchi M.,Foca G.,Lucisano M.,Marchetti A.,Pagani M.A.,Tassi L.,Ulrici A..Classification of Cereal Flours by Chemometric Analysis of MIR Spectra.J.Agric.Food Chem.[J].2004,52(5):1062-1067
[23] Reid L.M.,O'Donnell C.P.,Kelly J.D.,Downey G..Preliminary Studies for the Differentiation of Apple Juice Samples by Chemometric Analysis of Solid-Phase Microextraction-Gas Chromatographic Data.J.Agric.Food Che -m.[J].2004,52(23):6891-6896
[24] Reid L.M.,O'Donnell C.P.,Downey G..Potential of SPME-GC and Chemometrics To Detect Adulteration of Soft Fruit Purées. J.Agric. Food Chem.[J].2004,52(3):421-427
[25] Kelly J.F.D.,Downey G..Detection of Sugar Adulterants in Apple Juice Using Fourier Transform Infrared Spectroscopy and Chemometrics.J.Agric. Food Chem.[J].2005,53(9):3281-3286
[26] Cozzolino D.,Smyth H.E.,Gishen M..Feasibility Study on the Use of Visible and Near-Infrared Spectroscopy Together with Chemometrics To Discriminate between Commercial White Wines of Different Varietal Origins.J.Agric.Food Chem.[J].2003,51(26):7703-7708
[27] Cianchetta G.,Mannhold R.,Cruciani G.,Baroni M.,Cecchetti V.Chem -ometric Studies on the Bactericidal Activity of Quinolones via and Extended VolSurf Approach.J.Med.Chem.[J].2004,47(12):3193-3201
[28] Togkalidou T.,Tung H.H.,Sun Y.,Andrews A.,Braatz R.D..Solution Concentration Prediction for Pharmaceutical Crystallization Processes Using Robust Chemometrics and ATR FTIR Spectroscopy. Org. Process Res. Dev.[J].2002,6(3):317-322
[29] Matter H.,Schwab W.Affinity and Selectivity of Matrix Metallopro -teinase Inhibitors:A Chemometrical Study from the Perspective of Ligands and Proteins.J.Med.Chem.[J].1999,42(22):4506-4523
[30] Rantanen J.,Wikstrom H.,Turner R.,Taylor L.S..Use of In-Line Near-Infrared Spectroscopy in Combination with Chemometrics for Improved Understanding of Pharmaceutical Processes. Anal.Chem. [J].2005, 77(2):556-563
[31] Ivo Moret,Andrea Gambaro,Rossano Piazza,Fabiana Corami,Cesare Ravazzi,Carlo Andreoli,Cristina Truzzi,Luca Lambertucci,Giuseppe Scarp -oni.Chemometric Studies in the Lagoon of Venice,Italy.Annual Evolution of Sulphur Species and Relationship to Biogeochemical Cycles in Lagoon Water.Annalidi Chimica.[J].2004,94(5):373-387
[32] Plegge V.,Slama,M.,Suselbeck B.,Wienke D,Spener F,Knoll M, Zabo -rosch C.Analysis of Ternary Mixtures with a Single Dynamic Microbial Sensor and Chemometrics Using a Nonlinear Multivariate Calibration. Anal.Chem.[J].2000,72(13):2937-2942
[33] Gardner W.P.,Shaffer R.E.,Girard J.E.,Callahan J.H.Application of Quantitative Chemometric Analysis Techniques to Direct Sampling Mass Spectrometry.Anal.Chem.[J].2001,73(3):596-605
[34] Slama M.,Zaborosch C.,Wienke D.,Spener F..Simultaneous Mixture Analysis Using a Dynamic Microbial Sensor Combined with Chemo -metrics. Anal.Chem.[J].1996,68(21):3845-3850
[35] 吴海龙,梁逸曾,分析试验室,1999, 18(6), 94
[36] 朱立峰,浙江大学硕士学位论文,2002,杭州。
[37] Kramer R., Chemometric Techniques for Quantitative Analysis,Marcek Dekker, New York, 1998
[38] Martens H., Naes T., Multivariate Calibration, 1989, Wiely, New York
[39] Indahl U. G., Narinder S. Sahni, Bente Kirkhus, Tormod Nxs. Chemom. Intell. Lab. Syst.,1999, 49, 19
[40] 吉海彦,严衍禄,分析测试学报,1999, 18(3), 11
[41] Brown C. W., Lo S. C., Anal. Chem.,1998, 70, 2983
[42] Wang Y., Veltkamp D. J., Kolwaski B. R., Anal. Chem.,1991, 63, 2750
[43] Blank T. B., Sum S. T., Brown S. D., et al, Anal. Chem.,1996, 68, 2987
[44] A. C. Olivieri, J. Chemometrics, 2002, 16, 207
[45] 胡育筑.化学计量学简明教程[M].北京,中国医药科技出版社.1997,第一版,2-3
[46] 崔卉,梁逸曾,现代分析化学与化学计量学,分析化学,1996,24(8):974-980
[47] 李华昌,符斌,化学计量学在分析化学中的应用,冶金分析[J],1995,15(1):33-40
[48] 李梦龙,李志良,汪敬,分析化学计量学,分析实验室[J],1995,14(3):101-110
[49] 李梦龙,张晓彤,孙兆林,汪敬,分析化学计量学,分析实验室[J],1997,16(3):101-110
[50] 吴海龙,梁逸曾,俞汝勤,分析化学计量学,分析实验室[J],1999,18(6):94-102
[51] Michael Slama,Christiane Zaborosch,Dietrich Wienke,and Friedrich Spener. Simultaneous Mixture Analysis Using a Dynamic Microbial SensorCombined with Chemometrics.Analytical Chemistry[J].1996,68(21): 3845 -3850
[52] Seasholtz M. B., Kowalski B. R., Anal. Chim. Acta, 1993, 277, 165
[53] Candolfi A. De Maesschalck R. Jouan-Rimbaud D., et al, Journal of Pharmaceutical and Biomedical Analysis, 1999, 21(1), 115
[1] McClure WF,Anal Chem,1994,66(1):43A.
[2] Abney W, Festing ER, Philos. Trans. R. Soc.,1881, 172:887.
[3] Brackett FS, Proc. Natl. Acad. Sci. (US), 1928, 14:857.
[4] Kaye W, Spectrochim Acta, 1954, 6:257.
[5] Kaye W, Spectrochim Acta, 1955, 7:181.
[6] Birth GS, Norris KH, Food Technol., 1958, 12:592.
[7] Norris KH, Rowan JD, Agric. Eng., 1962, 43:154.
[8]Hart JR, Norris KH, Golumbic C, Cereal Chem., 1962, 39:64.
[9] Massie DR, Norris KH, Trans. Am. Soc. Agric. Eng.,1965,8:598.
[10] Norris KH, Agric. Eng., 1964, 45:154.
[11] Whetsel KB, Appl. Spectrosc. Rew., 1968, 2:1.
[12] Birth GS, Zachariah GL, Trans. Am. Soc. Agric. Eng., 1973, 16:371.
[13] Watson C, Anal. Chem., 1977, 49:835A.
[14] Williams PC, Stevenson SG, Irvine GN, Cereal Chem., 1978, 55:263.
[15] Birth GS, J. Food Sci., 1979, 44:949.
[16] McClure WF, Hamid A, Am. Lab., 1980, 12:57.
[17] Davies AMC, Williams P, Near Infrared Spectroscopy: The Future Waves, Chichester, UK: NIR Publications, 1996,334.
[18] G. Bologna, Artificial Intelligence in Medicine, 2003, 28:141.
[19] Polaskova P, Bocaz G, Li H, Havel J, J. Chromatography A, 2002, 979:59.
[20] Amari T, Ozaki Y, Appl. Spectrosc., 2002,56(3):350.
[21] 袁洪福,陆婉珍,石油冶炼与化工,1998,29(9):27.
[22] 袁洪福,龙义成,陆婉珍,ZL96218361X,1997.
[23] 袁洪福,陆婉珍,现代科学仪器,1998,5:6.
[24] 阎衍禄,现代科学仪器,1992,32:27.
[25] Ingle JDJr, Crouch SR, Spectrochemical Analysis, New York: Prentice-Hall Englewood Cliffs, NJ, 1988.
[26] Osborne BG, Fearn T, Near Infrared Spectroscopy in Food Analysis, New York: John Woley and Sons Inc., 1986.
[27] 吴谨光主编,近代傅立叶变换红外光谱技术及应用,北京:科学技术文献出版社,1994,255.
[28] Wheeler OH, Chem. Educ., 1960, 37:234.
[29] Honigs DE,Hieftje GM, Mark HL, Hirschfeld TB, Anal.Chem., 1985,57:2299.
[30] Mark HL, Tunnell D, Anal. Chem., 1985, 57:1449.
[31] Ilari J, Martens H, Isaksson T, Appl. Spectrosc., 1998,42:722.
[32] Aucott L, Garthwaite P, Analyst, 1988, 113:1849.
[33] Ciurcazk E, Torlini R, Demkowicz M, Spectrosc., 1986,1(7):36.
[34] Hildrum KI, Isaksson T, Nares T, Tandberg A, Near Infrared Spectroscopy Bridging the Gap between Data Analysis and Application, New York: Ellis Horwood, 1992,400.
[35] 袁洪福,陆婉珍,现代科学仪器,1998,5:6.
[36] Wu W, et at, Anal. Chim. Acta, 1995, 315:243.
[37] Qin SJ, et al, Computer Chem. Eng., 1992, 16(4):379.
[38] Geladi P, Kowalski B, Anal. Chim. Acta, 1986, 185:19.
[39] Kania AL, CDU and FCCU Optimization Using NIR Technology, Annual Meeting, National Petroleum Refiners Association, San Antonio, Texas, 1997.
[40] Kelly JJ, Barlow CH , Jinguji TM , Callis JB. Anal Chem., 1989, 61:313.
[41] Kelly JJ, Callis JB, Anal Chem., 1990, 62:1444.
[42] Swarin SJ, Drumm CA, Spectroscopy, 1992, 7(7):42.
[43] Bohacs G, Ovadi Z, J. Near Infrared Spectrosc., 1998,6:341.
[44] Steven JC. Anal. Chem., 1996, 68 (20):3525.
[45] Cooper JB , Wise KL , Welch WT, Bledsoe RR, Sumner M S. Appl. Spectrosc., 1996, 50(7):917.
[46] Asker N, Kokot S, Appl. Spectrosc., 1991, 45(7):115.
[47] A J Rest ,R Warren and S C Murray. A ppl . S pect rosc. , 1996 ,50 (4) :517
[48] M L Lysaght . Fuel , 1993 ,72 (5) :623
[49] R C Murray and M S Zetter. USP 2 ,512 ,751
[50] A Espinosn ,D C Lambert and A Martens. USP 5 ,475 ,612
[51] J Michael and Boyd. WO94/ 17391
[52] D C Lambert and B Sylvie. EP 0 706 050 A1
[53] C Ralston ,X Wu and O C Mullins. Appl. Spectrosc., 1996 ,50 (12) :1563
[54] O Marvik. Process Cont rol Qual . , 1997 ,9 (4) :127
[55] Takahashi ,Masakuzu ,Hajika et al,Near Infrared Spectroscopy ,The Future Waves ,7th Proc. Int . Conf., Near Infrared Spectroscopy,Davies A M C and Williams Phil ,1996 ,494～497.
[56] E Johnsen. Process Cont rol Qual., 1997, 9 (4) :205
[57] L S Chin. Near Infrared Spectroscopy: The Future Waves ,7th Proc. Int . Conf. Near Infrared Spectroscopy, Davies A M C and Williams Phil ,1996 ,441～447.
[58] J J Pascual ,P Molina and R Puchades. Near Infrared Spectroscopy: The Future Waves, 7th Proc. Int. Conf. Near Infrared Spectroscopy, Davies A, M C and Williams Phil,1996 ,559～564
[59] M Meurens ,WJ Li ,M Foulon et al. Cerevisia, 1995, 20 (3):33
[60] T Zahn. Near Infrared Transmittance Spectroscopy, A Powerful Tool for Fast Process and Product Control in Breweries and Malthouses. Monogr.Eur.Brew. Conv. 1993 ( E.B.C. Symposium Instrumentation and Measurement ,1992) :23～31
[61] GAbraham ,P Gabor and A C Sidney. Near Infrared Spectroscopy : TheFutur Waves ,7th Proc. Int . Conf . Near Infrared Spectroscopy, Davies A M C and Williams Phil ,1996 ,323～327
[62] M Kathleen. Near Infrared Spectroscopy: The Future Waves, 7th Proc. Int. Conf. Near Infrared Spectroscopy, Davies A M C and Williams Phil, 1996 ,328～333.
[63] J S Tong, M Meurens and H Noel. Near Infrared Spectroscopy, The Future Waves ,7th Proc. Int . Conf. Near Infrared Spectroscopy ,Davies A M C and Williams Phil ,1996 ,334～336.
[64] D Gyula, J Janos and V N Istvan. Near Infrared Spectroscopy, The Future Waves,7th Proc. Int. Conf. Near Infrared Spectroscopy, Davies A M C and Williams Phil ,1996 :353～356
[65] M R Gatin, J R Long ,P W Schmitt. Near Infrared Spectroscopy, The Future Waves, 7th Proc. Int. Conf. Near Infrared Spectroscopy, Davies A M C and Williams Phil ,1996 ,347～352
[66] D A Burns and E W Ciurczak. Handbook of Near2Inf rared A nalysis , Marcel Dekker INC. , 1992 ,565
[67] M Shimoyama and T Ninomiya. J . Near Inf rared S pect rosc. , 1998 ,6 :317
[68] G Lachenal. J . Near Inf rared S pect rosc. , 1998 ,6 :299
[69] R Classon. Chemical Engineering , 1993 , (3) :102
[70] O A Lang. Hydrocarbon Processing , 1994 ,Feb. :69
[71] A Espinosa ,D C Lambert and M Valleur. Hydrocarbon Processing , 1995 ,Feb. :86
[72] C H Twu and J E Coon. Hydrocarbon Processing , 1996 ,Feb. :51
[73] I Zilberman ,J Bigman and I Sela. Hydrocarbon Processing , 1996 ,May :91
[74] D Lambert ,B Descales ,S Bages ,S Bages et al. Hydrocarbon Processing , 1995 ,Dec. :103
[75] A Kania. CDU and FCCU Optimization using NIR Technology , 1997 NPRA ,Mar. 16
[76] Yukihiro O, et al, Keza Keukya,1992,20(11):889.
[77] Blanco M, et al, Anal.Chim.Acta,1994,298(2):183.
[78] Morisseau KM, et al, Drug Dev. Ind. Pharm.,1995,21(9):107
[1] 罗工亮.从神经网络到支撑向量机(上).冶金自动化.2001,No5:1-5.
[2] 罗工亮.从神经网络到支撑向量机(中).冶金自动化.2001,No6:1-4.
[3] 罗工亮.从神经网络到支撑向量机(下).冶金自动化.2002,No7:1-5.
[4] 张 学 工 . 关 于 统 计 学 习 理 论 与 支 持 向 量 机 . 自 动 化 学报.2000,26(1):32-42.
[5] 边肇棋,张学工编.模式识别.北京:清华大学出版社,2000.
[6] Vapnik V. N., The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
[7] T.Mitchell. Machine Learning.McGraw-Hill,1997.
[8] O.L.Mangasarian.Generalized support vector machines. Technical Report Mathematical Programming TR 98-14,University of Wisconsin in Madison,1998.
[9] V.Vapnik and A.Chervonenkis. On the uniform convergence of relative freguencies of events to their probabilities.Theory of Probability and its Applications,16(2):264-280,1971.
[10] A.Blumer, A.Ehrenfeucht, D.Haussler,and M.K.Warmuth.Learnaility and the Vapnik-Chervonrnkis dimension. Journal of the ACM, 36(4): 929-965,1989.
[11] V.Vapnik.Estimation of Dependences Based on Empirical Data {in Russian}.Nauka,1979.(English translation Springer Verlag,1982).
[12] V.Vapnik.Statistical Learning Theory.Wiley,1998.
[13] B.E.Boser,I.M.Guyon,and V.N.Vapnik.A training algorithm for optimal margin classifiers. In D.Haussler,editor,Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory,pages 144-152. ACM Press,1992.
[14] V.Vapnik.Statistical Learning Theory.Wiley,1998.
[15] J. K. Anlauf and M. Behl. The adatron : an adaptive perceptron algori -thm. Europhysics Letters, 10:687-692, 1989.
[16] R.O.Duda and P.E.Hart.Pattern Classification and Scene Analysis. Wiley,1973.
[17] V.Vapnik and A.Lerner.Pattern recognition using generalized portrait method.Automation and Remote Control,24,1963.
[18] C.M.Bishop.Neural Networks for Pattern Recognition.Clarendon Press, 1995.
[19] S.Arora,L.Babai,J.Stern,and Z.Sweedyk.Hardness of approximate opti -ma in lattices,codes and linear systems. Journale of Computer and System Sciences,54(2):317-331,1997.
[20] K.U.Hoffgen,K.S.van Horn, and H.U.Simon.Robust trainability of single neurons. Journal of Computer and System Sciences, 50(1): 114-125,1995.
[21] R.Fletcher.Practical methods of Optimization. Wiley,1998.
[22] V .Vapnik.The Nature of Statistical Learrning Theory. Springer Verlag, 1995.
[23] M.Bazaraa,D.Sherali,and C.Shetty.Nonlinear Programming:Theory and Algorithms. Wiley-Interscience Series in Discrete Mathematics and Optimization.Wiley,1992.
[24] H.Kuhn and A.Tucker. Nonlinear programming. In Proceedings of 2nd Berkeley Symposium on Mathematical and Probabilistics,pages 481 -492.University of California Press, 1951.
[25] W. Karush.Minima of Functions of Several Variables with Inequalities as Side Comstraints. Deparment of Mathematics, University of Chicago, 1939.MSc Thesis.
[26] D.Luenberger. Linear and Nonlinear Programming. Addison -Wsley, 1984.
[27] O.L. Mangasarian. Linear and nonlinear separation of patterns by linear programming.Operations Research,13:44-452,1965.
[28] T.M.Cover.Geometrical and statistical properties of systems of linear inequalities with application in pattern recognition.IEEE Transactions on Electronic Computers,14:326-334,1965.
[29] M.H.Hassoun. Optical Threshold Gates and Logical Signal Processing. PhD thesis,Department of ECE, Wayne State University, Detroit, USA, 1986.
[30] N.Aronszajn. Theory of Reproducing Kemels.Transactions of the Ame -rican Mathematical Society,68: 337-404, 1950.
[31] G.Wahba.Spline Models for Observational Data,volume 59 of CBMS -NSF Regional Conference Series in Applied Mathematics. SIAM,1990.
[32] T.Poggio and F.Girosi.Networks for approximation and learning. Proceedings of the IEEE,78(9),September 1990.
[33] M. Aizerman, E. Braverman, and L. Rozonoer. Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning [J]. Automation and Remote Control, 25:821-837, 1964.
[34] B.E.Boser,I.M.Guyon,and V.N.Vapnik.A training algorithm for optimal margin classifiers. In D.Haussler,editor,Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory,pages 144-152. ACM Press,1992.
[35] T.Poggio. On optimal nonlinear associative recall.Biological Cyberbetu -cs,19:201-209,1975.
[36] Cortes C, Vapnik V. Support Vector Networks [J]. Machine Learning, 1995,20 :273–297
[37] Burges C. J. C., Schêlkopf B. Improving The Accuracy and Speed of Support Vector Learning Machines [A]. Advances in Neural Informa -tion Processing Systems [C]. Cambridge, MA: MIT Press , 1997. 1375 -3811
[38] Pontil M.,Verri A. Support Vector Machines for 3 - d Object Recog-nition [J]. IEEE Trans. PAMI , 1998 ,20(5) :637–646.
[39] 李晓黎,刘继敏,史忠值. 基于支持向量机与无监督聚类相结合的中文网页分类器[J] .计算机学报,2001 ,24(1) :62–68.
[40] Gish H., Schimdt M. Text - Indepentent Speaker Identification [J] .IEEE Trans. Signal Processing Magazine,1994,42(1):18–32.
[41] Osuna E., Freund R., Girosi F. Training Support Vector Machines: An Application to Face Detection [A] . Proceedings of IEEE Conference on CVPR’97[C]. Puerto Rico,1997.130–136.
[42] Kumar V , Poggio T. Learning - Based Approach to Real Time Tracking and Analysis of Faces [A] . Proceedings of the Fourth International Con -ference on Face and Gesture Recognition [C]. France : Grenoble , 2000. v96–101.
[43] Huang J . Face Pose Discrimination Using Support Vector Machines [A] .Proceedings of 14TH International Conference on Pattern Reco -gnition[C]. 1998.154–156.
[44] Walavalkar L. Support Vector Learning for Gender Classification Using Audio and Visual Cues [J]. International Journal of Pattern Recognition and Artificial Intelligence , 2003 ,17(3): 417–439.
[45] Mukherjee S. Classifying Microarray Data Using Support Vector Machines [A]. A Practical Approach to Microarray Data Analysis [C]. Boston:Kcuwer Academic ,2003:166–185.
[46] Zhao Q , Principe J C. SVM for SAR ATR [J]. IEEE. Trans. AES, 2001 , 37(2) :643 – 653.
[47] Brown M, Lewis H G, Gunn S R. Linear Spectral Mixture Models and Support Vector Machines for Remote Sensing [J].IEEE Trans. Geosci-ence and Remote Sensing ,1998 ,38(5) :2346 – 2360.
[48] Vapnik V N , Golowich S , Smola A. Support Vector Method for Func -tion Approximation, Regression , Estimation , and Signal Processing [A].Advances in Neural Information Processing Systems[C].Cambridge, MA: MIT Press ,1997.281 – 287.
[49] Weston J. Support Vector Density Estimation [A]. Advances in Kernel Methods: Support Vector Learning [C]. Cambridge, MA: MIT Press. 1999.293–306.
[50] Müller K R , Smola A J , Rtsch G, et al. Predicting Time Series With Support Vector Machines [A]. Proceedings ofICANN’97[C] .Springer ,1997.999–1005.
[51] Suykens J A K. Optimal Control By Least Squares Support Vector Machines [J]. Neural Networks, 2001,14(1):23–25.
[52] Burges C J C. A Tutorial on Support Vector Machines for Pattern Recognition [J]. Data Mining and Knowledge Discovery , 1998 , 2(2) : 121–167.
[53] Haiqin Yang, Laiwan Chan ,Irwin King. Support Vector Machine Regression for Volatile Stock Market Prediction [A]. Third International Conference on Intelligent Data Engineering and Automated Learning (IDEAL’02) [C]. Springer, 2002.391-396.
[54] Mike Fugate, James R ,Gattikers. Computer Intrusion Detection with Classification and Anomaly Detection Using SVMs [J]. International Journal of Pattern Recognition and Artificial Intelligence, 2003, 17(3): 441–458.
[55] Osuna E, Freund R. Training Support Vector Machines :an Applicationto Face Detection [A] . Proc. of Computer Vision and Pattern Recog nition [C].San J uan ,Puerto Rico ,IEEE Computer Soc., 1997: 130-136
[56] 马勇,丁晓青. 基于层次型支持向量机的人脸检测[J] . 清华大学学报(自然科学版),2003,43(1):35-38.
[57] 叶航军,白雪生,徐光祐. 基于支持向量机的人脸姿态判定[J] . 清华大学学报(自然科学版),2003,43(1):67-70
[58] 周志明,王以治,黄文芝,王宁宁. 计算机工程与应用[J],基于小波和支持向量机的人脸识别技术. 2004(12):52-54.
[59] 代六玲,黄河燕,陈肇雄. 一种文本分类的在线SVM学习算法[J].中文信息学报,2005,19(5):11-15.
[60] 李珩,朱靖波,姚天顺. 基于SVM 的中文组块分析[J].中文信息学报,2004,18 (2):1-7.
[61] 李春花,卢正鼎.一种基于支持向量机的图像数字水印算法[J ].中国图像图形学报,2006,11(9):1322-1326.
[62] 段丹青,陈松乔,杨卫平. 网络入侵中的支持向量机主动学习算法[J].计算机工程与应用,2006(1):117-120.
[63] 孙红星,赵楠楠,徐心和.基于SVM 的模糊推理在图像降噪中的建模与仿真[J].系统仿真学报,2006,18(11):3276-3279.
[64] 白鹏, 刘君华.SVM在混合气体光谱中的应用[J].仪器仪表学报,2006,27(10):1242-1247.
[1] 中华人民共和国卫生部药典委员会编,中华人民共和国 2005 年版药典一部,北京化工出版社,2005
[2] 国家药品监督管理局编,化学药品地标升国标 12 册
[3] C.J.C.Burges.A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery,2 (2): 121 -167, 1998.
[4] 国家药品监督管理局编,化学药品地标升国标, WS1 -C 2-0001-89
[5]C.C. Chang, C.J. Lin, 台湾大学, 2005. 网址为http://www. csie.ntu. edu. tw/_cjlin/libsvm.
[6] 国家药品监督管理局编,化学药品地标升国标
[7] 中华人民共和国卫生部药典委员会编,中华人民共和国 1985 版药典二部,北京化工出版社,1985,附录,95
[1] 中华人民共和国卫生部药典委员会编,中华人民共和国 2005 年版药典一部,北京化工出版社,2005
[2] 国家药品监督管理局编,化学药品地标升国标 12 册
[3] C.J.C.Burges.A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery,2(2):121-167,1998.
[4] 中华人民共和国卫生部药典委员会编,中华人民共和国 2000 版药典二部,北京化工出版社,2000,711.
[5]C.C. Chang, C.J. Lin, 台湾大学, 2005. 网址为 http://www.csie. ntu. edu.tw/_cjlin/libsvm.
[6] 中华人民共和国卫生部药典委员会编,中华人民共和国 2000 版药典二部,北京化工出版社,2000,711.
[7] 中华人民共和国卫生部药典委员会编,中华人民共和国 1990 版药典一部,北京化工出版社,1990,附录,50.
[1] 中华人民共和国卫生部药典委员会编,中华人民共和国 2005 年版药典一部,北京化工出版社,2005
[2] 国家药品监督管理局编,化学药品地标升国标 12 册
[3] C.J.C.Burges.A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2): 121-167, 1998.
[4] 中华人民共和国卫生部药典委员会编,中华人民共和国 2000 版药典一部,北京化工出版社,2000,717.
[5] C.C. Chang, C.J. Lin, 台湾大学, 2005. 网址为 http://www.csie.ntu. edu. tw/_cjlin/libsvm.
[6] 中华人民共和国卫生部药典委员会编,中华人民共和国 2000 版药典一部,北京化工出版社,2000,717.
[7] 中华人民共和国卫生部药典委员会编,中华人民共和国 1985 版药典二部,北京化工出版社,1985,附录ⅤD.
[2] Words,“Chemometries:what do we mean with it, and What do we want from it?”Paper of InCINC’94; httP://www.emsl.pnl.gov:2080/docs/incinc/homepage.html
[3] Steven D.Brown,Stephen T.Sum,Frederic Despagne and Barry K.Lavine.Chemometrics.Anal.Chem.[J].1996,68:21R-61R
[4] Barry K.Lavine.Chemometrics.Anal.Chem.[J].1998,70:209R-228R
[5] Barry K.Lavine.Chemometrics.Anal.Chem.[J].2000,72:91R-97R
[6] Barry Lavine, and Jerome J.Workman, Jr. Chemometrics. Anal. Chem. [J]. 2004, 76:3365-3372
[7] 许禄,化学计量学方法[M].科学出版社,1995,1
[8] 梁逸曾, 白灰黑复杂多组分分析体系及其化学计量学算法,长沙,湖南科学技术出版社, 1996
[9] Latinen H.A., Anal.Chem., 1980, 52: 602A
[10] 中国科学院长春应用化学研究所,“现在分析化学规划”,1996
[11] 高鸿,“分析化学现状与未来”(“分析化学前沿”主题报告),北京.科学出版社,1991
[12] The Chemieal Soeiety, J. Chem. Inf. ComP. Sci., 1975,15:201
[13] Murry A., Anal. Chem., 1991,63:271A
[14] LiangYizeng, Kvalheim O.M., Manne R.,Chemom.Intell.Lab.Sys., 1993, 18:235
[15] 国家自然科学基金会,“自然科学学科发展战略调研报告”,“分析化学”, 北京, 科学出版社, 1993
[16] Howery D.G., Journal of Chem Educ.1983,60: 656
[17] Massart, D.L., Vandeginste, B.G.M., Buydens, L.M.C., de Jong, S., Lewi, P.J., Smeyers-Verbeke, J.Handbook of chemometries and qualimetries. Ainsterdam: Elsevier Scienee, 1997.
[18] 梁逸曾,俞汝勤.分析化学手册一化学计量学(第十分册),第二版.北京:化学工业出版社,2000.
[19] Wbld, S.Chemmetries; what do we mean with it, and what do we want from it? Chemom.Intell.Lab.Syst.1995, 30(l):109.
[20] Bacci M.,Picollo M.,Porcinai S.,Radicati B..Tempera-Painted Dosim -eters for Environmental Indoor Monitoring:A Spectroscopic and Chem ometric Approach. Environ.Sci.Technol[J]. 2000,34(13): 2859 -2865
[21] María Valeria Amé,María del Pilar Díaz,Daniel Alberto Wunder -lin.Occurrence of toxic cyanobacterial blooms in San Roque Reservoir (Córdoba,Argentina):A field and chemometricstudy.Environmental Toxico -logy[J].2003,18(3):192-201
[22] Cocchi M.,Foca G.,Lucisano M.,Marchetti A.,Pagani M.A.,Tassi L.,Ulrici A..Classification of Cereal Flours by Chemometric Analysis of MIR Spectra.J.Agric.Food Chem.[J].2004,52(5):1062-1067
[23] Reid L.M.,O'Donnell C.P.,Kelly J.D.,Downey G..Preliminary Studies for the Differentiation of Apple Juice Samples by Chemometric Analysis of Solid-Phase Microextraction-Gas Chromatographic Data.J.Agric.Food Che -m.[J].2004,52(23):6891-6896
[24] Reid L.M.,O'Donnell C.P.,Downey G..Potential of SPME-GC and Chemometrics To Detect Adulteration of Soft Fruit Purées. J.Agric. Food Chem.[J].2004,52(3):421-427
[25] Kelly J.F.D.,Downey G..Detection of Sugar Adulterants in Apple Juice Using Fourier Transform Infrared Spectroscopy and Chemometrics.J.Agric. Food Chem.[J].2005,53(9):3281-3286
[26] Cozzolino D.,Smyth H.E.,Gishen M..Feasibility Study on the Use of Visible and Near-Infrared Spectroscopy Together with Chemometrics To Discriminate between Commercial White Wines of Different Varietal Origins.J.Agric.Food Chem.[J].2003,51(26):7703-7708
[27] Cianchetta G.,Mannhold R.,Cruciani G.,Baroni M.,Cecchetti V.Chem -ometric Studies on the Bactericidal Activity of Quinolones via and Extended VolSurf Approach.J.Med.Chem.[J].2004,47(12):3193-3201
[28] Togkalidou T.,Tung H.H.,Sun Y.,Andrews A.,Braatz R.D..Solution Concentration Prediction for Pharmaceutical Crystallization Processes Using Robust Chemometrics and ATR FTIR Spectroscopy. Org. Process Res. Dev.[J].2002,6(3):317-322
[29] Matter H.,Schwab W.Affinity and Selectivity of Matrix Metallopro -teinase Inhibitors:A Chemometrical Study from the Perspective of Ligands and Proteins.J.Med.Chem.[J].1999,42(22):4506-4523
[30] Rantanen J.,Wikstrom H.,Turner R.,Taylor L.S..Use of In-Line Near-Infrared Spectroscopy in Combination with Chemometrics for Improved Understanding of Pharmaceutical Processes. Anal.Chem. [J].2005, 77(2):556-563
[31] Ivo Moret,Andrea Gambaro,Rossano Piazza,Fabiana Corami,Cesare Ravazzi,Carlo Andreoli,Cristina Truzzi,Luca Lambertucci,Giuseppe Scarp -oni.Chemometric Studies in the Lagoon of Venice,Italy.Annual Evolution of Sulphur Species and Relationship to Biogeochemical Cycles in Lagoon Water.Annalidi Chimica.[J].2004,94(5):373-387
[32] Plegge V.,Slama,M.,Suselbeck B.,Wienke D,Spener F,Knoll M, Zabo -rosch C.Analysis of Ternary Mixtures with a Single Dynamic Microbial Sensor and Chemometrics Using a Nonlinear Multivariate Calibration. Anal.Chem.[J].2000,72(13):2937-2942
[33] Gardner W.P.,Shaffer R.E.,Girard J.E.,Callahan J.H.Application of Quantitative Chemometric Analysis Techniques to Direct Sampling Mass Spectrometry.Anal.Chem.[J].2001,73(3):596-605
[34] Slama M.,Zaborosch C.,Wienke D.,Spener F..Simultaneous Mixture Analysis Using a Dynamic Microbial Sensor Combined with Chemo -metrics. Anal.Chem.[J].1996,68(21):3845-3850
[35] 吴海龙,梁逸曾,分析试验室,1999, 18(6), 94
[36] 朱立峰,浙江大学硕士学位论文,2002,杭州。
[37] Kramer R., Chemometric Techniques for Quantitative Analysis,Marcek Dekker, New York, 1998
[38] Martens H., Naes T., Multivariate Calibration, 1989, Wiely, New York
[39] Indahl U. G., Narinder S. Sahni, Bente Kirkhus, Tormod Nxs. Chemom. Intell. Lab. Syst.,1999, 49, 19
[40] 吉海彦,严衍禄,分析测试学报,1999, 18(3), 11
[41] Brown C. W., Lo S. C., Anal. Chem.,1998, 70, 2983
[42] Wang Y., Veltkamp D. J., Kolwaski B. R., Anal. Chem.,1991, 63, 2750
[43] Blank T. B., Sum S. T., Brown S. D., et al, Anal. Chem.,1996, 68, 2987
[44] A. C. Olivieri, J. Chemometrics, 2002, 16, 207
[45] 胡育筑.化学计量学简明教程[M].北京,中国医药科技出版社.1997,第一版,2-3
[46] 崔卉,梁逸曾,现代分析化学与化学计量学,分析化学,1996,24(8):974-980
[47] 李华昌,符斌,化学计量学在分析化学中的应用,冶金分析[J],1995,15(1):33-40
[48] 李梦龙,李志良,汪敬,分析化学计量学,分析实验室[J],1995,14(3):101-110
[49] 李梦龙,张晓彤,孙兆林,汪敬,分析化学计量学,分析实验室[J],1997,16(3):101-110
[50] 吴海龙,梁逸曾,俞汝勤,分析化学计量学,分析实验室[J],1999,18(6):94-102
[51] Michael Slama,Christiane Zaborosch,Dietrich Wienke,and Friedrich Spener. Simultaneous Mixture Analysis Using a Dynamic Microbial SensorCombined with Chemometrics.Analytical Chemistry[J].1996,68(21): 3845 -3850
[52] Seasholtz M. B., Kowalski B. R., Anal. Chim. Acta, 1993, 277, 165
[53] Candolfi A. De Maesschalck R. Jouan-Rimbaud D., et al, Journal of Pharmaceutical and Biomedical Analysis, 1999, 21(1), 115
[1] McClure WF,Anal Chem,1994,66(1):43A.
[2] Abney W, Festing ER, Philos. Trans. R. Soc.,1881, 172:887.
[3] Brackett FS, Proc. Natl. Acad. Sci. (US), 1928, 14:857.
[4] Kaye W, Spectrochim Acta, 1954, 6:257.
[5] Kaye W, Spectrochim Acta, 1955, 7:181.
[6] Birth GS, Norris KH, Food Technol., 1958, 12:592.
[7] Norris KH, Rowan JD, Agric. Eng., 1962, 43:154.
[8]Hart JR, Norris KH, Golumbic C, Cereal Chem., 1962, 39:64.
[9] Massie DR, Norris KH, Trans. Am. Soc. Agric. Eng.,1965,8:598.
[10] Norris KH, Agric. Eng., 1964, 45:154.
[11] Whetsel KB, Appl. Spectrosc. Rew., 1968, 2:1.
[12] Birth GS, Zachariah GL, Trans. Am. Soc. Agric. Eng., 1973, 16:371.
[13] Watson C, Anal. Chem., 1977, 49:835A.
[14] Williams PC, Stevenson SG, Irvine GN, Cereal Chem., 1978, 55:263.
[15] Birth GS, J. Food Sci., 1979, 44:949.
[16] McClure WF, Hamid A, Am. Lab., 1980, 12:57.
[17] Davies AMC, Williams P, Near Infrared Spectroscopy: The Future Waves, Chichester, UK: NIR Publications, 1996,334.
[18] G. Bologna, Artificial Intelligence in Medicine, 2003, 28:141.
[19] Polaskova P, Bocaz G, Li H, Havel J, J. Chromatography A, 2002, 979:59.
[20] Amari T, Ozaki Y, Appl. Spectrosc., 2002,56(3):350.
[21] 袁洪福,陆婉珍,石油冶炼与化工,1998,29(9):27.
[22] 袁洪福,龙义成,陆婉珍,ZL96218361X,1997.
[23] 袁洪福,陆婉珍,现代科学仪器,1998,5:6.
[24] 阎衍禄,现代科学仪器,1992,32:27.
[25] Ingle JDJr, Crouch SR, Spectrochemical Analysis, New York: Prentice-Hall Englewood Cliffs, NJ, 1988.
[26] Osborne BG, Fearn T, Near Infrared Spectroscopy in Food Analysis, New York: John Woley and Sons Inc., 1986.
[27] 吴谨光主编,近代傅立叶变换红外光谱技术及应用,北京:科学技术文献出版社,1994,255.
[28] Wheeler OH, Chem. Educ., 1960, 37:234.
[29] Honigs DE,Hieftje GM, Mark HL, Hirschfeld TB, Anal.Chem., 1985,57:2299.
[30] Mark HL, Tunnell D, Anal. Chem., 1985, 57:1449.
[31] Ilari J, Martens H, Isaksson T, Appl. Spectrosc., 1998,42:722.
[32] Aucott L, Garthwaite P, Analyst, 1988, 113:1849.
[33] Ciurcazk E, Torlini R, Demkowicz M, Spectrosc., 1986,1(7):36.
[34] Hildrum KI, Isaksson T, Nares T, Tandberg A, Near Infrared Spectroscopy Bridging the Gap between Data Analysis and Application, New York: Ellis Horwood, 1992,400.
[35] 袁洪福,陆婉珍,现代科学仪器,1998,5:6.
[36] Wu W, et at, Anal. Chim. Acta, 1995, 315:243.
[37] Qin SJ, et al, Computer Chem. Eng., 1992, 16(4):379.
[38] Geladi P, Kowalski B, Anal. Chim. Acta, 1986, 185:19.
[39] Kania AL, CDU and FCCU Optimization Using NIR Technology, Annual Meeting, National Petroleum Refiners Association, San Antonio, Texas, 1997.
[40] Kelly JJ, Barlow CH , Jinguji TM , Callis JB. Anal Chem., 1989, 61:313.
[41] Kelly JJ, Callis JB, Anal Chem., 1990, 62:1444.
[42] Swarin SJ, Drumm CA, Spectroscopy, 1992, 7(7):42.
[43] Bohacs G, Ovadi Z, J. Near Infrared Spectrosc., 1998,6:341.
[44] Steven JC. Anal. Chem., 1996, 68 (20):3525.
[45] Cooper JB , Wise KL , Welch WT, Bledsoe RR, Sumner M S. Appl. Spectrosc., 1996, 50(7):917.
[46] Asker N, Kokot S, Appl. Spectrosc., 1991, 45(7):115.
[47] A J Rest ,R Warren and S C Murray. A ppl . S pect rosc. , 1996 ,50 (4) :517
[48] M L Lysaght . Fuel , 1993 ,72 (5) :623
[49] R C Murray and M S Zetter. USP 2 ,512 ,751
[50] A Espinosn ,D C Lambert and A Martens. USP 5 ,475 ,612
[51] J Michael and Boyd. WO94/ 17391
[52] D C Lambert and B Sylvie. EP 0 706 050 A1
[53] C Ralston ,X Wu and O C Mullins. Appl. Spectrosc., 1996 ,50 (12) :1563
[54] O Marvik. Process Cont rol Qual . , 1997 ,9 (4) :127
[55] Takahashi ,Masakuzu ,Hajika et al,Near Infrared Spectroscopy ,The Future Waves ,7th Proc. Int . Conf., Near Infrared Spectroscopy,Davies A M C and Williams Phil ,1996 ,494～497.
[56] E Johnsen. Process Cont rol Qual., 1997, 9 (4) :205
[57] L S Chin. Near Infrared Spectroscopy: The Future Waves ,7th Proc. Int . Conf. Near Infrared Spectroscopy, Davies A M C and Williams Phil ,1996 ,441～447.
[58] J J Pascual ,P Molina and R Puchades. Near Infrared Spectroscopy: The Future Waves, 7th Proc. Int. Conf. Near Infrared Spectroscopy, Davies A, M C and Williams Phil,1996 ,559～564
[59] M Meurens ,WJ Li ,M Foulon et al. Cerevisia, 1995, 20 (3):33
[60] T Zahn. Near Infrared Transmittance Spectroscopy, A Powerful Tool for Fast Process and Product Control in Breweries and Malthouses. Monogr.Eur.Brew. Conv. 1993 ( E.B.C. Symposium Instrumentation and Measurement ,1992) :23～31
[61] GAbraham ,P Gabor and A C Sidney. Near Infrared Spectroscopy : TheFutur Waves ,7th Proc. Int . Conf . Near Infrared Spectroscopy, Davies A M C and Williams Phil ,1996 ,323～327
[62] M Kathleen. Near Infrared Spectroscopy: The Future Waves, 7th Proc. Int. Conf. Near Infrared Spectroscopy, Davies A M C and Williams Phil, 1996 ,328～333.
[63] J S Tong, M Meurens and H Noel. Near Infrared Spectroscopy, The Future Waves ,7th Proc. Int . Conf. Near Infrared Spectroscopy ,Davies A M C and Williams Phil ,1996 ,334～336.
[64] D Gyula, J Janos and V N Istvan. Near Infrared Spectroscopy, The Future Waves,7th Proc. Int. Conf. Near Infrared Spectroscopy, Davies A M C and Williams Phil ,1996 :353～356
[65] M R Gatin, J R Long ,P W Schmitt. Near Infrared Spectroscopy, The Future Waves, 7th Proc. Int. Conf. Near Infrared Spectroscopy, Davies A M C and Williams Phil ,1996 ,347～352
[66] D A Burns and E W Ciurczak. Handbook of Near2Inf rared A nalysis , Marcel Dekker INC. , 1992 ,565
[67] M Shimoyama and T Ninomiya. J . Near Inf rared S pect rosc. , 1998 ,6 :317
[68] G Lachenal. J . Near Inf rared S pect rosc. , 1998 ,6 :299
[69] R Classon. Chemical Engineering , 1993 , (3) :102
[70] O A Lang. Hydrocarbon Processing , 1994 ,Feb. :69
[71] A Espinosa ,D C Lambert and M Valleur. Hydrocarbon Processing , 1995 ,Feb. :86
[72] C H Twu and J E Coon. Hydrocarbon Processing , 1996 ,Feb. :51
[73] I Zilberman ,J Bigman and I Sela. Hydrocarbon Processing , 1996 ,May :91
[74] D Lambert ,B Descales ,S Bages ,S Bages et al. Hydrocarbon Processing , 1995 ,Dec. :103
[75] A Kania. CDU and FCCU Optimization using NIR Technology , 1997 NPRA ,Mar. 16
[76] Yukihiro O, et al, Keza Keukya,1992,20(11):889.
[77] Blanco M, et al, Anal.Chim.Acta,1994,298(2):183.
[78] Morisseau KM, et al, Drug Dev. Ind. Pharm.,1995,21(9):107
[1] 罗工亮.从神经网络到支撑向量机(上).冶金自动化.2001,No5:1-5.
[2] 罗工亮.从神经网络到支撑向量机(中).冶金自动化.2001,No6:1-4.
[3] 罗工亮.从神经网络到支撑向量机(下).冶金自动化.2002,No7:1-5.
[4] 张 学 工 . 关 于 统 计 学 习 理 论 与 支 持 向 量 机 . 自 动 化 学报.2000,26(1):32-42.
[5] 边肇棋,张学工编.模式识别.北京:清华大学出版社,2000.
[6] Vapnik V. N., The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
[7] T.Mitchell. Machine Learning.McGraw-Hill,1997.
[8] O.L.Mangasarian.Generalized support vector machines. Technical Report Mathematical Programming TR 98-14,University of Wisconsin in Madison,1998.
[9] V.Vapnik and A.Chervonenkis. On the uniform convergence of relative freguencies of events to their probabilities.Theory of Probability and its Applications,16(2):264-280,1971.
[10] A.Blumer, A.Ehrenfeucht, D.Haussler,and M.K.Warmuth.Learnaility and the Vapnik-Chervonrnkis dimension. Journal of the ACM, 36(4): 929-965,1989.
[11] V.Vapnik.Estimation of Dependences Based on Empirical Data {in Russian}.Nauka,1979.(English translation Springer Verlag,1982).
[12] V.Vapnik.Statistical Learning Theory.Wiley,1998.
[13] B.E.Boser,I.M.Guyon,and V.N.Vapnik.A training algorithm for optimal margin classifiers. In D.Haussler,editor,Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory,pages 144-152. ACM Press,1992.
[14] V.Vapnik.Statistical Learning Theory.Wiley,1998.
[15] J. K. Anlauf and M. Behl. The adatron : an adaptive perceptron algori -thm. Europhysics Letters, 10:687-692, 1989.
[16] R.O.Duda and P.E.Hart.Pattern Classification and Scene Analysis. Wiley,1973.
[17] V.Vapnik and A.Lerner.Pattern recognition using generalized portrait method.Automation and Remote Control,24,1963.
[18] C.M.Bishop.Neural Networks for Pattern Recognition.Clarendon Press, 1995.
[19] S.Arora,L.Babai,J.Stern,and Z.Sweedyk.Hardness of approximate opti -ma in lattices,codes and linear systems. Journale of Computer and System Sciences,54(2):317-331,1997.
[20] K.U.Hoffgen,K.S.van Horn, and H.U.Simon.Robust trainability of single neurons. Journal of Computer and System Sciences, 50(1): 114-125,1995.
[21] R.Fletcher.Practical methods of Optimization. Wiley,1998.
[22] V .Vapnik.The Nature of Statistical Learrning Theory. Springer Verlag, 1995.
[23] M.Bazaraa,D.Sherali,and C.Shetty.Nonlinear Programming:Theory and Algorithms. Wiley-Interscience Series in Discrete Mathematics and Optimization.Wiley,1992.
[24] H.Kuhn and A.Tucker. Nonlinear programming. In Proceedings of 2nd Berkeley Symposium on Mathematical and Probabilistics,pages 481 -492.University of California Press, 1951.
[25] W. Karush.Minima of Functions of Several Variables with Inequalities as Side Comstraints. Deparment of Mathematics, University of Chicago, 1939.MSc Thesis.
[26] D.Luenberger. Linear and Nonlinear Programming. Addison -Wsley, 1984.
[27] O.L. Mangasarian. Linear and nonlinear separation of patterns by linear programming.Operations Research,13:44-452,1965.
[28] T.M.Cover.Geometrical and statistical properties of systems of linear inequalities with application in pattern recognition.IEEE Transactions on Electronic Computers,14:326-334,1965.
[29] M.H.Hassoun. Optical Threshold Gates and Logical Signal Processing. PhD thesis,Department of ECE, Wayne State University, Detroit, USA, 1986.
[30] N.Aronszajn. Theory of Reproducing Kemels.Transactions of the Ame -rican Mathematical Society,68: 337-404, 1950.
[31] G.Wahba.Spline Models for Observational Data,volume 59 of CBMS -NSF Regional Conference Series in Applied Mathematics. SIAM,1990.
[32] T.Poggio and F.Girosi.Networks for approximation and learning. Proceedings of the IEEE,78(9),September 1990.
[33] M. Aizerman, E. Braverman, and L. Rozonoer. Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning [J]. Automation and Remote Control, 25:821-837, 1964.
[34] B.E.Boser,I.M.Guyon,and V.N.Vapnik.A training algorithm for optimal margin classifiers. In D.Haussler,editor,Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory,pages 144-152. ACM Press,1992.
[35] T.Poggio. On optimal nonlinear associative recall.Biological Cyberbetu -cs,19:201-209,1975.
[36] Cortes C, Vapnik V. Support Vector Networks [J]. Machine Learning, 1995,20 :273–297
[37] Burges C. J. C., Schêlkopf B. Improving The Accuracy and Speed of Support Vector Learning Machines [A]. Advances in Neural Informa -tion Processing Systems [C]. Cambridge, MA: MIT Press , 1997. 1375 -3811
[38] Pontil M.,Verri A. Support Vector Machines for 3 - d Object Recog-nition [J]. IEEE Trans. PAMI , 1998 ,20(5) :637–646.
[39] 李晓黎,刘继敏,史忠值. 基于支持向量机与无监督聚类相结合的中文网页分类器[J] .计算机学报,2001 ,24(1) :62–68.
[40] Gish H., Schimdt M. Text - Indepentent Speaker Identification [J] .IEEE Trans. Signal Processing Magazine,1994,42(1):18–32.
[41] Osuna E., Freund R., Girosi F. Training Support Vector Machines: An Application to Face Detection [A] . Proceedings of IEEE Conference on CVPR’97[C]. Puerto Rico,1997.130–136.
[42] Kumar V , Poggio T. Learning - Based Approach to Real Time Tracking and Analysis of Faces [A] . Proceedings of the Fourth International Con -ference on Face and Gesture Recognition [C]. France : Grenoble , 2000. v96–101.
[43] Huang J . Face Pose Discrimination Using Support Vector Machines [A] .Proceedings of 14TH International Conference on Pattern Reco -gnition[C]. 1998.154–156.
[44] Walavalkar L. Support Vector Learning for Gender Classification Using Audio and Visual Cues [J]. International Journal of Pattern Recognition and Artificial Intelligence , 2003 ,17(3): 417–439.
[45] Mukherjee S. Classifying Microarray Data Using Support Vector Machines [A]. A Practical Approach to Microarray Data Analysis [C]. Boston:Kcuwer Academic ,2003:166–185.
[46] Zhao Q , Principe J C. SVM for SAR ATR [J]. IEEE. Trans. AES, 2001 , 37(2) :643 – 653.
[47] Brown M, Lewis H G, Gunn S R. Linear Spectral Mixture Models and Support Vector Machines for Remote Sensing [J].IEEE Trans. Geosci-ence and Remote Sensing ,1998 ,38(5) :2346 – 2360.
[48] Vapnik V N , Golowich S , Smola A. Support Vector Method for Func -tion Approximation, Regression , Estimation , and Signal Processing [A].Advances in Neural Information Processing Systems[C].Cambridge, MA: MIT Press ,1997.281 – 287.
[49] Weston J. Support Vector Density Estimation [A]. Advances in Kernel Methods: Support Vector Learning [C]. Cambridge, MA: MIT Press. 1999.293–306.
[50] Müller K R , Smola A J , Rtsch G, et al. Predicting Time Series With Support Vector Machines [A]. Proceedings ofICANN’97[C] .Springer ,1997.999–1005.
[51] Suykens J A K. Optimal Control By Least Squares Support Vector Machines [J]. Neural Networks, 2001,14(1):23–25.
[52] Burges C J C. A Tutorial on Support Vector Machines for Pattern Recognition [J]. Data Mining and Knowledge Discovery , 1998 , 2(2) : 121–167.
[53] Haiqin Yang, Laiwan Chan ,Irwin King. Support Vector Machine Regression for Volatile Stock Market Prediction [A]. Third International Conference on Intelligent Data Engineering and Automated Learning (IDEAL’02) [C]. Springer, 2002.391-396.
[54] Mike Fugate, James R ,Gattikers. Computer Intrusion Detection with Classification and Anomaly Detection Using SVMs [J]. International Journal of Pattern Recognition and Artificial Intelligence, 2003, 17(3): 441–458.
[55] Osuna E, Freund R. Training Support Vector Machines :an Applicationto Face Detection [A] . Proc. of Computer Vision and Pattern Recog nition [C].San J uan ,Puerto Rico ,IEEE Computer Soc., 1997: 130-136
[56] 马勇,丁晓青. 基于层次型支持向量机的人脸检测[J] . 清华大学学报(自然科学版),2003,43(1):35-38.
[57] 叶航军,白雪生,徐光祐. 基于支持向量机的人脸姿态判定[J] . 清华大学学报(自然科学版),2003,43(1):67-70
[58] 周志明,王以治,黄文芝,王宁宁. 计算机工程与应用[J],基于小波和支持向量机的人脸识别技术. 2004(12):52-54.
[59] 代六玲,黄河燕,陈肇雄. 一种文本分类的在线SVM学习算法[J].中文信息学报,2005,19(5):11-15.
[60] 李珩,朱靖波,姚天顺. 基于SVM 的中文组块分析[J].中文信息学报,2004,18 (2):1-7.
[61] 李春花,卢正鼎.一种基于支持向量机的图像数字水印算法[J ].中国图像图形学报,2006,11(9):1322-1326.
[62] 段丹青,陈松乔,杨卫平. 网络入侵中的支持向量机主动学习算法[J].计算机工程与应用,2006(1):117-120.
[63] 孙红星,赵楠楠,徐心和.基于SVM 的模糊推理在图像降噪中的建模与仿真[J].系统仿真学报,2006,18(11):3276-3279.
[64] 白鹏, 刘君华.SVM在混合气体光谱中的应用[J].仪器仪表学报,2006,27(10):1242-1247.
[1] 中华人民共和国卫生部药典委员会编,中华人民共和国 2005 年版药典一部,北京化工出版社,2005
[2] 国家药品监督管理局编,化学药品地标升国标 12 册
[3] C.J.C.Burges.A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery,2 (2): 121 -167, 1998.
[4] 国家药品监督管理局编,化学药品地标升国标, WS1 -C 2-0001-89
[5]C.C. Chang, C.J. Lin, 台湾大学, 2005. 网址为http://www. csie.ntu. edu. tw/_cjlin/libsvm.
[6] 国家药品监督管理局编,化学药品地标升国标
[7] 中华人民共和国卫生部药典委员会编,中华人民共和国 1985 版药典二部,北京化工出版社,1985,附录,95
[1] 中华人民共和国卫生部药典委员会编,中华人民共和国 2005 年版药典一部,北京化工出版社,2005
[2] 国家药品监督管理局编,化学药品地标升国标 12 册
[3] C.J.C.Burges.A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery,2(2):121-167,1998.
[4] 中华人民共和国卫生部药典委员会编,中华人民共和国 2000 版药典二部,北京化工出版社,2000,711.
[5]C.C. Chang, C.J. Lin, 台湾大学, 2005. 网址为 http://www.csie. ntu. edu.tw/_cjlin/libsvm.
[6] 中华人民共和国卫生部药典委员会编,中华人民共和国 2000 版药典二部,北京化工出版社,2000,711.
[7] 中华人民共和国卫生部药典委员会编,中华人民共和国 1990 版药典一部,北京化工出版社,1990,附录,50.
[1] 中华人民共和国卫生部药典委员会编,中华人民共和国 2005 年版药典一部,北京化工出版社,2005
[2] 国家药品监督管理局编,化学药品地标升国标 12 册
[3] C.J.C.Burges.A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2): 121-167, 1998.
[4] 中华人民共和国卫生部药典委员会编,中华人民共和国 2000 版药典一部,北京化工出版社,2000,717.
[5] C.C. Chang, C.J. Lin, 台湾大学, 2005. 网址为 http://www.csie.ntu. edu. tw/_cjlin/libsvm.
[6] 中华人民共和国卫生部药典委员会编,中华人民共和国 2000 版药典一部,北京化工出版社,2000,717.
[7] 中华人民共和国卫生部药典委员会编,中华人民共和国 1985 版药典二部,北京化工出版社,1985,附录ⅤD.