基于光谱分析的燃油组分检测技术研究
摘要
燃油组分和品质直接关系到其燃烧效率、发动机寿命以及尾气排放造成的环境污染,现有的标准检测方法存在成本高、速度慢、检测过程复杂等局限性。光谱分析技术具有快速、无损、低成本、安全可靠等许多现有的标准方法不可比拟的优势。本文采用近红外和拉曼光谱分析技术,结合样本分类、线性和非线性校正模型、异常样本剔除等方法,建立了汽柴油组分和品质指标的定量分析模型,并开发了相应的快速检测仪,具体研究内容包括：
1、提出了一种结合支持向量机(SVM)分类模型的甲醇汽油组分近红外光谱检测方法。通过测量甲醇汽油的近红外光谱,并采用微分和标准正态变换对谱图进行了预处理；对甲醇汽油和不含甲醇的成品汽油建立了SVM分类模型,将甲醇汽油和成品汽油区分开；然后对于甲醇汽油,通过谱图相关性分析找到甲醇含量对应的特征波段,并建立了最小二乘支持向量机(LSSVM)定量分析模型。实验结果表明,采用上述近红外光谱法检测甲醇汽油中的甲醇含量可以达到很高的预测精度,预测复相关系数R2达到0.9926,均方预测误差(RMSEP)为0.58%(体积)。
2、将结合偏最小二乘(PLS)特征提取的LSSVM算法(PLS-LSSVM)应用于柴油品质的近红外光谱检测。基于预处理后的柴油近红外光谱,分别建立了柴油十六烷值、硫含量、密度、50%回收温度的PLS-LSSVM定量校正模型,并与常用的PLS、LSSVM等方法进行了比较。实验结果表明,PLS-LSSVM模型集成了PLS与SVM模型的优势,能够减少样品待测属性与光谱之间非线性程度的影响。
3、采用光栅色散型拉曼光谱,结合异常样本检测技术,建立了汽油芳烃含量、烯烃含量和氧含量的PLS定量分析模型。通过剔除个别异常样本,有效地提高了预测模型精度,得到了较好的预测效果。其中汽油芳烃含量模型R2达到0.9965, RMSEP为0.3；烯烃含量模型R2达到0.9274,RMSEP为0.52；氧含量模型R2达到0.9843,RMSEP为0.083。实验结果表明：采用拉曼光谱分析技术可以有效的解决汽油族组成的定量分析问题,其分析精度显著高于近红外光谱法,同时也适用于汽油生产过程中的在线分析。
4.自主开发研制了基于近红外光谱的便携式甲醇汽油快速检测仪。针对目前市场上缺乏有效的甲醇汽油品质快速检测仪的现状,自行开发研制了基于光栅色散型近红外光谱仪的便携式甲醇汽油快速检测仪,并已应用于多个甲醇汽油生产企业。现场实际应用结果表明：该仪器对工业现场生产的甲醇汽油和成品汽油的分类正确,甲醇含量检测精度高,重复性好,操作和维护简便,能够满足国内甲醇生产企业、油库油站等用户对甲醇汽油甲醇含量快速检测的需求。
The composition and quality of engine fuel directly influence its combustion efficiency, engine life and emissions which could cause the environmental pollution. The existing standard testing methods for engine fuel are generally slow, complex and high-cost. Spectral analysis technology is fast, nondestructive, low cost and safe, which makes it better than many existing standard methods. This thesis applies near infrared (NIR) and Raman spectroscopy in quantitative analysis of engine fuel composition and quality indices by combining with sample classification, calibration modeling and outlier detection techniques. Moreover, a fast NIR analyzer for methanol-gasoline is developed. Detailed research contents include:
1. A detection method for methanol-gasoline composition is proposed based on NIR spectroscopy with support vector machine (SVM) classification model. Firstly, the NIR spectra of the methanol gasoline is measured and pre-processed by using derivative and standard normal variate. Secondly, an SVM classification model to distinguish methanol gasoline and regular gasoline without methanol is built. Thirdly, the feature wavelength of methanol content is found by correlation analysis between the spectra and methanol content. Finally, a least square SVM (LSSVM) quantitative model for the methanol content is established. Experimental results show that using NIR spectroscopy to detect the methanol content in methanol-gasoline can achieve high predictive accuracy. The corresponding multiple correlation coefficient of prediction (R2) is up to 0.9926; the root mean square error of prediction (RMSEP) is 0.58% (volume).
2. The LSSVM algorithm with PLS feature extraction (PLS-LSSVM) is applied to the NIR spectroscopy detection of diesel quality. Based on the pre-processed diesel NIR spectrum, PLS-LSSVM quantitative models of diesel quality properties, such as cetane number, sulfur content, density and 50% recycling temperature, are respectively established. Compared with common-used PLS, LSSVM and other models, the results show that the PLS-LSSVM model integrates the benefits of PLS and SVM algorithms, which can reduce the influence of nonlinear degree between the properties and spectra of samples.
3. By using a dispersive Raman spectrometer, PLS quantitative analysis models of gasoline aromatics content, olefin content and oxygen content are established combining with outlier detection. By removing the outlier samples, the prediction model accuracy is effectively improved. The RMSEP of aromatics, olefin and oxygen content are 0.30,0.52 and 0.083 respectively, and the corresponding R2 are 0.997, 0.927 and 0.984 respectively. Experimental results show the effectiveness of Raman spectroscopy to analyze the hydrocarbon group of gasoline, and the analysis accuracy is significantly higher than that of NIR spectroscopy.
4. A portable rapid NIR analyzer for methanol gasoline is developed because of the strong absorption of methanol in NIR spectra. To meet the need of rapid detection of the methanol content, a portable fast analyzer based on dispersive NIR spectrometer is independently developed. It has been applied in several production enterprises of methanol gasoline. Practical application results show that this instrument can correctly classify methanol gasoline and regular gasoline, and it can precisely detect the methanol content. It is easy to operate and maintain this instrument. All these advantages make it to satisfy the needs of the methanol-gasoline production enterprises and other users in China.
1、提出了一种结合支持向量机(SVM)分类模型的甲醇汽油组分近红外光谱检测方法。通过测量甲醇汽油的近红外光谱,并采用微分和标准正态变换对谱图进行了预处理；对甲醇汽油和不含甲醇的成品汽油建立了SVM分类模型,将甲醇汽油和成品汽油区分开；然后对于甲醇汽油,通过谱图相关性分析找到甲醇含量对应的特征波段,并建立了最小二乘支持向量机(LSSVM)定量分析模型。实验结果表明,采用上述近红外光谱法检测甲醇汽油中的甲醇含量可以达到很高的预测精度,预测复相关系数R2达到0.9926,均方预测误差(RMSEP)为0.58%(体积)。
2、将结合偏最小二乘(PLS)特征提取的LSSVM算法(PLS-LSSVM)应用于柴油品质的近红外光谱检测。基于预处理后的柴油近红外光谱,分别建立了柴油十六烷值、硫含量、密度、50%回收温度的PLS-LSSVM定量校正模型,并与常用的PLS、LSSVM等方法进行了比较。实验结果表明,PLS-LSSVM模型集成了PLS与SVM模型的优势,能够减少样品待测属性与光谱之间非线性程度的影响。
3、采用光栅色散型拉曼光谱,结合异常样本检测技术,建立了汽油芳烃含量、烯烃含量和氧含量的PLS定量分析模型。通过剔除个别异常样本,有效地提高了预测模型精度,得到了较好的预测效果。其中汽油芳烃含量模型R2达到0.9965, RMSEP为0.3；烯烃含量模型R2达到0.9274,RMSEP为0.52；氧含量模型R2达到0.9843,RMSEP为0.083。实验结果表明：采用拉曼光谱分析技术可以有效的解决汽油族组成的定量分析问题,其分析精度显著高于近红外光谱法,同时也适用于汽油生产过程中的在线分析。
4.自主开发研制了基于近红外光谱的便携式甲醇汽油快速检测仪。针对目前市场上缺乏有效的甲醇汽油品质快速检测仪的现状,自行开发研制了基于光栅色散型近红外光谱仪的便携式甲醇汽油快速检测仪,并已应用于多个甲醇汽油生产企业。现场实际应用结果表明：该仪器对工业现场生产的甲醇汽油和成品汽油的分类正确,甲醇含量检测精度高,重复性好,操作和维护简便,能够满足国内甲醇生产企业、油库油站等用户对甲醇汽油甲醇含量快速检测的需求。
The composition and quality of engine fuel directly influence its combustion efficiency, engine life and emissions which could cause the environmental pollution. The existing standard testing methods for engine fuel are generally slow, complex and high-cost. Spectral analysis technology is fast, nondestructive, low cost and safe, which makes it better than many existing standard methods. This thesis applies near infrared (NIR) and Raman spectroscopy in quantitative analysis of engine fuel composition and quality indices by combining with sample classification, calibration modeling and outlier detection techniques. Moreover, a fast NIR analyzer for methanol-gasoline is developed. Detailed research contents include:
1. A detection method for methanol-gasoline composition is proposed based on NIR spectroscopy with support vector machine (SVM) classification model. Firstly, the NIR spectra of the methanol gasoline is measured and pre-processed by using derivative and standard normal variate. Secondly, an SVM classification model to distinguish methanol gasoline and regular gasoline without methanol is built. Thirdly, the feature wavelength of methanol content is found by correlation analysis between the spectra and methanol content. Finally, a least square SVM (LSSVM) quantitative model for the methanol content is established. Experimental results show that using NIR spectroscopy to detect the methanol content in methanol-gasoline can achieve high predictive accuracy. The corresponding multiple correlation coefficient of prediction (R2) is up to 0.9926; the root mean square error of prediction (RMSEP) is 0.58% (volume).
2. The LSSVM algorithm with PLS feature extraction (PLS-LSSVM) is applied to the NIR spectroscopy detection of diesel quality. Based on the pre-processed diesel NIR spectrum, PLS-LSSVM quantitative models of diesel quality properties, such as cetane number, sulfur content, density and 50% recycling temperature, are respectively established. Compared with common-used PLS, LSSVM and other models, the results show that the PLS-LSSVM model integrates the benefits of PLS and SVM algorithms, which can reduce the influence of nonlinear degree between the properties and spectra of samples.
3. By using a dispersive Raman spectrometer, PLS quantitative analysis models of gasoline aromatics content, olefin content and oxygen content are established combining with outlier detection. By removing the outlier samples, the prediction model accuracy is effectively improved. The RMSEP of aromatics, olefin and oxygen content are 0.30,0.52 and 0.083 respectively, and the corresponding R2 are 0.997, 0.927 and 0.984 respectively. Experimental results show the effectiveness of Raman spectroscopy to analyze the hydrocarbon group of gasoline, and the analysis accuracy is significantly higher than that of NIR spectroscopy.
4. A portable rapid NIR analyzer for methanol gasoline is developed because of the strong absorption of methanol in NIR spectra. To meet the need of rapid detection of the methanol content, a portable fast analyzer based on dispersive NIR spectrometer is independently developed. It has been applied in several production enterprises of methanol gasoline. Practical application results show that this instrument can correctly classify methanol gasoline and regular gasoline, and it can precisely detect the methanol content. It is easy to operate and maintain this instrument. All these advantages make it to satisfy the needs of the methanol-gasoline production enterprises and other users in China.
引文
[1]GB 17930-2006《车用汽油》[S].
[2]GB 19147-2009《车用柴油》[S].
[3]DB33/T 756.1-2009《车用甲醇汽油第一部分：M15》[S].
[4]Squillace P. J., Zogorski J. S., Wilber W. G., et al. Preliminary assessment of the occurrence and possible sources of MtBE in groundwater in the United States, 1993-1994[J]. Environ. Sci. Technol,1996,30 (5):1721-1730.
[5]杨永坛.催化柴油中含氮化合物类型分布的气相色谱分析方法[J].色谱,2008,26(4)：478-483.
[6]Diehl J. W., Di Sanzo F. P. Determination of aromatic hydrocarbons in gasolines by flow modulated comprehensive two-dimensional gas chromatography[J]. Journal of Chromatography A,2005,1080 (2):157-165.
[7]Micyus N. J., McCurry J. D., Seeley J. V. Analysis of aromatic compounds in gasoline with flow-switching comprehensive two-dimensional gas chromatography [J]. Journal of Chromatography A,2005,1086 (1-2):115-121.
[8]Sciarrone D., Tranchida P. Q., Ragonese C., et al. Multidimensional GC coupled to MS for the simultaneous determination of oxygenate compounds and BTEX in gasoline[J]. Journal of Separation Science,2010,33 (4-5):594-599.
[9]Chopra A., Singh D., Kalsi W. R., et al. Determination of Fatty Acid Based Lubricity Improver in Diesel by GC[J]. Chromatographia,2009,70 (7-8): 1143-1146.
[10]Elghawi U., Theinnoi K., Sitshebo S., et al. GC-MS determination of low hydrocarbon species (C-1-C-6) from a diesel partial oxidation reformer[J]. International Journal of Hydrogen Energy,2008,33 (23):7074-7083.
[11]Hatanaka R. R., Flumignan D. L., de Oliveira J. E. GC Fingerprints Coupled to Pattern-Recognition Multivariate SIMCA Chemometric Analysis for Brazilian Gasoline Quality Studies[J]. Chromatographia,2009,70 (7-8):1135-1142.
[12]Pavlova A., Ivanova R. GC methods for quantitative determination of benzene in gasoline[J]. Acta Chromatographica,2003,13215-225.
[13]Skrobot V. L., Castro E. V. R., Pereira R. C. C., et al. Use of principal component analysis (PCA) and linear discriminant analysis (LDA) in gas chromatographic (GC) data in the investigation of gasoline adulteration[J]. Energy & Fuels,2007,21 (6):3394-3400.
[14]徐广通,袁洪福,陆婉珍.现代近红外光谱技术及应用进展[J].光谱学与光谱分析,2000,20(2)：134-142.
[15]Roldan P. S., Alcantara I. L., Padilha C. C. F., et al. Determination of copper, iron, nickel and zinc in gasoline by FAAS after sorption and preconcentration on silica modified with 2-aminotiazole groups[J]. Fuel,2005,84 (2-3):305-309.
[16]dos Santos D. S. S., Teixeira A. P., Barbosa J. T. P., et al. Use of cetyltrimethylammonium bromide as surfactant for the determination of copper and chromium in gasoline emulsions by electrothermal atomic absorption spectrometry[J]. Spectrochimica Acta Part B-Atomic Spectroscopy,2007,62 (9): 1072-1077.
[17]de Jesus A., Silva M. M., Vale M. G. R. The use of microemulsion for determination of sodium and potassium in biodiesel by flame atomic absorption spectrometry[J]. Talanta,2008,74 (5):1378-1384.
[18]Santelli R. E., Bezerra M. A., Freire A. S., et al. Non-volatile vanadium determination in petroleum condensate, diesel and gasoline prepared as detergent emulsions using GF AAS[J]. Fuel,2008,87 (8-9):1617-1622.
[19]Dittert I. M., Silva J. S. A., Araujo R. G. O., et al. Direct and simultaneous determination of Cr and Fe in crude oil using high-resolution continuum source graphite furnace atomic absorption spectrometry[J]. Spectrochimica Acta Part B-Atomic Spectroscopy,2009,64 (6):537-543.
[20]Lyra F. H., Carneiro M., Brandao G. P., et al. Determination of Na, K, Ca and Mg in biodiesel samples by flame atomic absorption spectrometry (F AAS) using microemulsion as sample preparation[J]. Microchemical Journal,2010,96 (1):180-185.
[21]Silva J. S. A., Chaves E. S., dos Santos E. J., et al. Calibration Techniques and Modifiers for the Determination of Cd, Pb and TI in Biodiesel as Microemulsion by Graphite Furnace Atomic Absorption Spectrometry[J]. Journal of the Brazilian Chemical Society,2010,21 (4):620-626.
[22]Torres D. P., Dittert I. M., Hohn H., et al. Determination of mercury in gasoline diluted in ethanol by GF AAS after cold vapor generation, pre-concentration in gold column and trapping on graphite tube[J]. Microchemical Journal,2010,96 (1):32-36.
[23]Chen Z. W., Wei F. Z., Radley I., et al. Low-level sulfur in fuel determination using monochromatic WD XRF-ASTM D 7039-04[J]. Elemental Analysis of Fuels and Lubricants:Recent Advances and Future Prospects,2005,1468 116-127.
[24]Miskolczi N., Bartha L., Antal F., et al. Determination of sulphur in liquids obtained by thermal cracking of waste polymers and commercial fuels with different analytical methods[J]. Talanta,2005,66 (5):1264-1271.
[25]Miskolczi N., Bartha L., Borszeki J., et al. Determination of sulfur content of diesel fuels and diesel fuel-like fractions of waste polymer cracking[J]. Talanta, 2006,69 (3):776-780.
[26]Sterry A. The influence of pressure and nitrogen compunds on the deep hydrodesulfurization of diesel fuels[J]. Revista De Chimie,2005,56 (4): 425-428.
[27]Teixeira L. S. G., Rocha R. B. S., Sobrinho E. V., et al. Simultaneous determination of copper and iron in automotive gasoline by X-ray fluorescence after pre-concentration on cellulose paper[J]. Talanta,2007,72 (3):1073-1076.
[28]Barker L. R., Kelly W. R., Guthrie W. F. Determination of sulfur in biodiesel and petroleum diesel by X-ray fluorescence (XRF) using the gravimetric standard addition method-Ⅱ[J]. Energy & Fuels,2008,22 (4):2488-2490.
[29]Zhang K. S., Hu J. N., Gao S. Z., et al. Sulfur content of gasoline and diesel fuels in northern China[J]. Energy Policy,2010,38 (6):2934-2940.
[30]He L., Kear-Padilla L., Lieberman S., et al. Rapid in situ determination of total oil concentration in water using ultraviolet fluorescence and light scattering coupled with artificial neural networks[J]. Analytica chimica acta,2003,478 (2): 245-258.
[31]Karlitschek P., Lewitzka F., B"1nting U., et al. Detection of aromatic pollutants in the environment by using UV-laser-induced fluorescence[J]. Applied Physics B:Lasers and Optics,1998,67 (4):497-504.
[32]Ozanyan K. B., Yeo T. L., Hindle F. P., et al. Fiber-based UV laser-diode fluorescence sensor for commercial gasolines[J]. Sensors Journal, IEEE,2004,4 (5):681-690.
[33]Soares I. P., Rezende T. F., Silva R. C., et al. Multivariate calibration by variable selection for blends of raw soybean oil/biodiesel from different sources using Fourier transform infrared spectroscopy (FTIR) spectra data[J]. Energy & Fuels, 2008,22 (3):2079-2083.
[34]Guarieiro L. L. N., Pinto A. C., de Aguiar P. F., et al. Determination of biodiesel percentage in biodiesel:diesel mixtures using mid-infrared spectroscopy [J]. Quimica Nova,2008,31 (2):421-426.
[35]Klingbeil A. E., Jeffries J. B., Hanson R. K. Temperature- and composition-dependent mid-infrared absorption spectrum of gas-phase gasoline: Model and measurements [J]. Fuel,2008,87 (17-18):3600-3609.
[36]Mulrooney J., Clifford J., Fitzpatrick C., et al. Detection of carbon dioxide emissions from a diesel engine using a mid-infrared optical fibre based sensor[J]. Sensors and Actuators a-Physical,2007,136 (1):104-110.
[37]Sumizawa H., Yamada H., Tonokura K. Real-time monitoring of nitric oxide in diesel exhaust gas by mid-infrared cavity ring-down spectroscopy[J]. Applied Physics B-Lasers and Optics,2010,100 (4):925-931.
[38]Kelly J. J., Barlow C. H., Jinguji T. M., et al. Prediction of gasoline octane numbers from near-infrared spectral features in the range 660-1215 nm[J]. Analytical chemistry,1989,61 (4):313-320.
[39]Kardamakis A. A., Pasadakis N. Autoregressive modeling of near-IR spectra and MLR to predict RON values of gasolines[J]. Fuel,2010,89 (1):158-161.
[40]Litani-Barzilai I., Sela I., Bulatov V., et al. On-line remote prediction of gasoline properties by combined optical methods[J]. Analytica chimica acta,1997,339 (1-2):193-199.
[41]Felicio C. C., Bras L. P., Lopes J. A., et al. Comparison of PLS algorithms in gasoline and gas oil parameter monitoring with MIR and NIR[J]. Chemometrics and intelligent laboratory systems,2005,78 (1-2):74-80.
[42]史月华,陆勇,徐光明,等.主成分回归残差神经网络校正算法用于近红外光谱快速测定汽油辛烷值[J].分析化学2001,29(1)：87-91.
[43]褚小立,田高友,袁洪福,等.小波变换结合多维偏最小二乘方法用于近红外光谱定量分析[J].分析化学,2006,34 175-178.
[44]Fernanda Pimentel M., Ribeiro G. M. G. S., da Cruz R. S., et al. Determination of biodiesel content when blended with mineral diesel fuel using infrared spectroscopy and multivariate calibration[J]. Microchemical Journal,2006,82 (2):201-206.
[45]孔翠萍,褚小立,杜泽学,等.近红外光谱方法预测生物柴油主要成分[J].分析化学,2010,38(6)：805-810.
[46]Breitkreitz M. C, Raimundo I. M., Rohwedder J. J. R., et al. Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration[J]. Analyst,2003,128(9):1204-1207.
[47]Felizardo P., Baptista P., Menezes J. C., et al. Multivariate near infrared spectroscopy models for predicting methanol and water content in biodiesel[J]. Analytica chimica acta,2007,595 (1-2):107-113.
[48]王艳斌,齐洪祥.人工神经网络用于近红外光谱测定柴油闪点[J].分析化学,2000,28(9)：1070-1073.
[49]姚肖刚,戴连奎,方骏.基于支持向量机的柴油十六烷值近红外光谱测量方法[J].化工自动化及仪表,2004,31(2)：48-51.
[50]Fernandes H. L., Ivo Jr M. R., Pasquini C., et al. Simultaneous determination of methanol and ethanol in gasoline using NIR spectroscopy:Effect of gasoline composition[J]. Talanta,2008,75 (3):804-810.
[51]Cooper J. B., Larkin C. M., Schmitigal J., et al. Rapid Analysis of Jet Fuel Using a Handheld Near-Infrared (NIR) Analyzer[J]. Applied spectroscopy,2011, 65(2):187-192.
[52]Cramer J. A., Morris R. E., Rose-Pehrsson S. L. Use of Genetic Algorithms To Improve Partial Least Squares Fuel Property and Synthetic Fuel Modeling from Near-Infrared Spectra[J]. Energy & Fuels,2010,24 5560-5572.
[53]Balabin R. M., Lomakina E. I., Safieva R. Z. Neural network (ANN) approach to biodiesel analysis:Analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy [J]. Fuel, 2011,90 (5):2007-2015.
[54]Balabin R. M., Safieva R. Z. Motor oil classification by base stock and viscosity based on near infrared (NIR) spectroscopy data[J]. Fuel,2008,87 (12): 2745-2752.
[55]Balabin R. M., Safieva R. Z. Gasoline classification by source and type based on near infrared (NIR) spectroscopy data[J]. Fuel,2008,87 (7):1096-1101.
[56]Balabin R. M., Safieva R. Z., Lomakina E. I. Gasoline classification using near infrared (NIR) spectroscopy data:Comparison of multivariate techniques[J]. Analytica chimica acta,2010,671 (1-2):27-35.
[57]Veras G., Gomes A. A., Silva A. C., et al. Classification of biodiesel using NIR spectrometry and multivariate techniques[J]. Talanta,2010,83 565-568.
[58]Xu Z. F., Bunker C. E., Harrington P. D. Classification of Jet Fuel Properties by Near-Infrared Spectroscopy Using Fuzzy Rule-Building Expert Systems and Support Vector Machines[J]. Applied spectroscopy,2010,64 (11):1251-1258.
[59]Gonzaga F. B., Pasquini C. A Low Cost Short Wave Near Infrared Spectrophotometer:Application for Determination of Quality Parameters of Diesel Fuel[J]. Analytica chimica acta,2010,670 (1-2):92-97.
[60]李淑玲.拉曼光谱仪及其应用进展[J].岩矿测试,1998,17(4)：312-316.
[61]胡军,胡继明.拉曼光谱在分析化学中的应用进展[J].分析化学,2000,28(6)：764-771.
[62]Gerrard D. L. Raman spectroscopy[J]. Analytical Chemistry,1994,66 (12): 547-557.
[63]McCreery R. L. Raman spectroscopy for chemical analysis[M]. Hobokon:John Wiley & Sons Inc 2000.
[64]Cooper J. B., Wise K. L., Groves J., et al. Determination of octane numbers and Reid Vapor pressure of commercial petroleum fuels using FT-Raman spectroscopy and partial least-squares regression analysis[J]. Analytical chemistry,1995,67 (22):4096-4100.
[65]Cooper J. B., Wise K. L., Welch W. T., et al. Determination of Weight Percent Oxygen in Commercial Gasoline:A Comparison between FT-Raman, FT-IR, and Dispersive Near-IR Spectroscopies[J]. Applied spectroscopy,1996,50 (7): 917-921.
[66]覃旭松.基于拉曼光谱的汽油辛烷值测定方法[D].浙江大学2004.
[67]包鑫,戴连奎.汽油多参数拉曼光谱分析仪中的稳健支持向量机方法[J].仪器仪表学报,2009,30(9)：1829-1835.
[68]淡图南,戴连奎.结合异常样本检测的汽油族组成拉曼光谱分析[J].光谱学与光谱分析,2010,30(4)：979-983.
[69]李晟,戴连奎.基于拉曼光谱的汽油牌号快速识别[J].光谱学与光谱分析,2010,30(11)：2993-2997.
[70]阮华,戴连奎.支持向量机分类与回归联合建模方法及其在拉曼光谱分析中的应用[J].仪器仪表学报,2010,31(11).
[71]Ku M. S., Hoeil C. Compositional analysis of naphtha by FT-Raman spectroscopy [J]. Bulletin-Korean Chemical Society,1999,20 159-162.
[72]Santos Jr V. O., Oliveira F. C. C., Lima D. G., et al. A comparative study of diesel analysis by FTIR, FTNIR and FT-Raman spectroscopy using PLS and artificial neural network analysis[J]. Analytica chimica acta,2005,547 (2): 188-196.
[73]Oliveira F. C. C., Brand o C. R. R., Ramalho H. F., et al. Adulteration of diesel/biodiesel blends by vegetable oil as determined by Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy[J]. Analytica chimica acta,2007,587 (2):194-199.
[74]Ye Q., Xu Q., Yu Y., et al. Rapid and quantitative detection of ethanol proportion in ethanol-gasoline mixtures by Raman spectroscopy[J]. Optics Communications,2009,282 (18):3785-3788.
[75]林艺玲,戴连奎,阮华.基于低分辨率色散型拉曼光谱仪的汽油苯含量快速分析[J].光谱学与光谱分析,2010,(11)：3002-3006.
[76]Stark E., Luchter K., Margoshes M. Near-infrared analysis (NIRA):A technology for quantitative and qualitative analysis[J]. Applied Spectroscopy Reviews,1986,22 (4):335-399.
[77]陆婉珍,强冬梅,徐广通,等.现代近红外光谱分析技术[M].北京：中国石化出版社,2000.
[78]Siesler H. W., Ozaki Y., Kawata S., et al. Near-infrared spectroscopy[M]. Weinheim:Wiley-VCH,2002.
[79]严衍禄.近红外光谱分析基础与应用[M].北京：中国轻工业出版社,2005.
[80]Brereton R. Chemometrics[J]. Chemistry in Britain,2000,36 (5):28-31.
[81]Brown S. D., Sum S. T., Despagne F., et al. Chemometrics[J]. Analytical chemistry,1996,68 (12):21-62.
[82]Chen Q., Zhao J., Fang C., et al. Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM)[J]. Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy,2007,66 (3):568-574.
[83]Lavine B. K. Chemometrics[J]. Analytical Chemistry,1998,70 (12):209-228.
[84]Lavine B. K. REVIEWS-Chemometrics[J]. Analytical Chemistry-Columbus, 2000,72 (12):91-97.
[85]Pasti L., Jouan-Rimbaud D., Massart D. L., et al. Application of Fourier transform to multivariatc calibration of near-infrared data[J]. Analytica chimica acta,1998,364 (1-3):253-263.
[86]Savitzky A., Golay M. J. E. Smoothing and differentiation of data by simplified least squares procedures[J]. Analytical chemistry,1964,36 (8):1627-1639.
[87]褚小立,袁洪福,陆婉珍.近红外分析中光谱预处理及波长选择方法进展与应用[J].化学进展,2004,16(4)：528-542.
[88]Barnes R., Dhanoa M., Lister S. J. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra[J]. Applied spectroscopy, 1989,43 (5):772-777.
[89]Dhanoa M., Lister S., Sanderson R., et al. The link between multiplicative scatter correction (MSC) and standard normal variate (SNV) transformations of NIR spectra[J]. Journal of Near Infrared Spectroscopy,1994,2 43-47.
[90]Wold H. Soft modeling by latent variables:the nonlinear iterative partial least squares approach[J]. Perspectives in probability and statistics, papers in honour of MS Bartlett,1975,520-540.
[91]Thomas E. V., Haaland D. M. Comparison of multivariate calibration methods for quantitative spectral analysis[J]. Analytical chemistry,1990,62 (10): 1091-1099.
[92]Thomas E. Gasohol:A green fuel[J]. Chemical engineering world,2003,38 (4): 37-38.
[93]Agarwal A. K. Biofuels (alcohols and biodiesel) applications as fuels for internal combustion engines[J]. Progress in Energy and Combustion Science, 2007,33 (3):233-271.
[94]申志兵,曹祖宾,赵金花,等.甲醇汽油使用性能的测试分析及综合评价[J].炼油技术与工程,2006,36(11)：49-51.
[95]张辉,李君,王立军,等.甲醇汽油发动机甲醛排放的测量与分析[J].吉林大学学报：工学版,2008,38(1)：32-36.
[96]吕胜春,李晖,祁东辉,等.甲醇／汽油混合燃料发动机非常规排放成分测量方法研究[J].内燃机学报,2006,24(1)：57-61.
[97]刘圣华,李晖,吕胜春,等.甲醇-汽油混合燃料对汽油机性能和排放的影响[J].西安交通大学学报,2006,40(1)：1-4.
[98]GB/T 23799-2009《车用甲醇汽油(M85)》[S].
[99]占细雄.光栅分光型乙醇汽油近红外光谱分析关键技术研究[D].吉林大学2008.
[100]淡图南,戴连奎.基于PLS投影分析的光谱波段选择方法[J].光谱学与光谱分析,2009,29(2)：351-354.
[101]Balabin R. M., Safieva R. Z., Lomakina E. I. Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction[J]. Chemometrics and intelligent laboratory systems,2007,88 (2):183-188.
[102]程丽华,吴金林.石油产品基础知识[M].北京：中国石化出版社,2006.
[103]GB/T 11132—2008《液体石油产品烃类的测定(荧光指示剂吸附法)》[S].
[104]SH/T 0741—2004《汽油中烃族组成测定法(多维气相色谱法)》)[S].
[105]褚小立,许育鹏,陆婉珍.支持向量回归建立成品汽油通用近红外校正模型的研究[J].分析测试学报,2008,27(6)：619-622.
[106]田高友,袁洪福,褚小立,等.结合小波变换与微分法改善近红外光谱分析精度[J].光谱学与光谱分析,2005,25(4)：516-520.
[107]史永刚,刘绍璞,宋世远,等.基于支持向量机的汽油族组成近红外光谱分 析方法研究[J].分析测试学报,2007,26(3)：343-346.
[108]柯以侃,董慧茹.分析化学手册,第三分册,光谱分析[M].北京：化学工业出版社,1998.
[2]GB 19147-2009《车用柴油》[S].
[3]DB33/T 756.1-2009《车用甲醇汽油第一部分：M15》[S].
[4]Squillace P. J., Zogorski J. S., Wilber W. G., et al. Preliminary assessment of the occurrence and possible sources of MtBE in groundwater in the United States, 1993-1994[J]. Environ. Sci. Technol,1996,30 (5):1721-1730.
[5]杨永坛.催化柴油中含氮化合物类型分布的气相色谱分析方法[J].色谱,2008,26(4)：478-483.
[6]Diehl J. W., Di Sanzo F. P. Determination of aromatic hydrocarbons in gasolines by flow modulated comprehensive two-dimensional gas chromatography[J]. Journal of Chromatography A,2005,1080 (2):157-165.
[7]Micyus N. J., McCurry J. D., Seeley J. V. Analysis of aromatic compounds in gasoline with flow-switching comprehensive two-dimensional gas chromatography [J]. Journal of Chromatography A,2005,1086 (1-2):115-121.
[8]Sciarrone D., Tranchida P. Q., Ragonese C., et al. Multidimensional GC coupled to MS for the simultaneous determination of oxygenate compounds and BTEX in gasoline[J]. Journal of Separation Science,2010,33 (4-5):594-599.
[9]Chopra A., Singh D., Kalsi W. R., et al. Determination of Fatty Acid Based Lubricity Improver in Diesel by GC[J]. Chromatographia,2009,70 (7-8): 1143-1146.
[10]Elghawi U., Theinnoi K., Sitshebo S., et al. GC-MS determination of low hydrocarbon species (C-1-C-6) from a diesel partial oxidation reformer[J]. International Journal of Hydrogen Energy,2008,33 (23):7074-7083.
[11]Hatanaka R. R., Flumignan D. L., de Oliveira J. E. GC Fingerprints Coupled to Pattern-Recognition Multivariate SIMCA Chemometric Analysis for Brazilian Gasoline Quality Studies[J]. Chromatographia,2009,70 (7-8):1135-1142.
[12]Pavlova A., Ivanova R. GC methods for quantitative determination of benzene in gasoline[J]. Acta Chromatographica,2003,13215-225.
[13]Skrobot V. L., Castro E. V. R., Pereira R. C. C., et al. Use of principal component analysis (PCA) and linear discriminant analysis (LDA) in gas chromatographic (GC) data in the investigation of gasoline adulteration[J]. Energy & Fuels,2007,21 (6):3394-3400.
[14]徐广通,袁洪福,陆婉珍.现代近红外光谱技术及应用进展[J].光谱学与光谱分析,2000,20(2)：134-142.
[15]Roldan P. S., Alcantara I. L., Padilha C. C. F., et al. Determination of copper, iron, nickel and zinc in gasoline by FAAS after sorption and preconcentration on silica modified with 2-aminotiazole groups[J]. Fuel,2005,84 (2-3):305-309.
[16]dos Santos D. S. S., Teixeira A. P., Barbosa J. T. P., et al. Use of cetyltrimethylammonium bromide as surfactant for the determination of copper and chromium in gasoline emulsions by electrothermal atomic absorption spectrometry[J]. Spectrochimica Acta Part B-Atomic Spectroscopy,2007,62 (9): 1072-1077.
[17]de Jesus A., Silva M. M., Vale M. G. R. The use of microemulsion for determination of sodium and potassium in biodiesel by flame atomic absorption spectrometry[J]. Talanta,2008,74 (5):1378-1384.
[18]Santelli R. E., Bezerra M. A., Freire A. S., et al. Non-volatile vanadium determination in petroleum condensate, diesel and gasoline prepared as detergent emulsions using GF AAS[J]. Fuel,2008,87 (8-9):1617-1622.
[19]Dittert I. M., Silva J. S. A., Araujo R. G. O., et al. Direct and simultaneous determination of Cr and Fe in crude oil using high-resolution continuum source graphite furnace atomic absorption spectrometry[J]. Spectrochimica Acta Part B-Atomic Spectroscopy,2009,64 (6):537-543.
[20]Lyra F. H., Carneiro M., Brandao G. P., et al. Determination of Na, K, Ca and Mg in biodiesel samples by flame atomic absorption spectrometry (F AAS) using microemulsion as sample preparation[J]. Microchemical Journal,2010,96 (1):180-185.
[21]Silva J. S. A., Chaves E. S., dos Santos E. J., et al. Calibration Techniques and Modifiers for the Determination of Cd, Pb and TI in Biodiesel as Microemulsion by Graphite Furnace Atomic Absorption Spectrometry[J]. Journal of the Brazilian Chemical Society,2010,21 (4):620-626.
[22]Torres D. P., Dittert I. M., Hohn H., et al. Determination of mercury in gasoline diluted in ethanol by GF AAS after cold vapor generation, pre-concentration in gold column and trapping on graphite tube[J]. Microchemical Journal,2010,96 (1):32-36.
[23]Chen Z. W., Wei F. Z., Radley I., et al. Low-level sulfur in fuel determination using monochromatic WD XRF-ASTM D 7039-04[J]. Elemental Analysis of Fuels and Lubricants:Recent Advances and Future Prospects,2005,1468 116-127.
[24]Miskolczi N., Bartha L., Antal F., et al. Determination of sulphur in liquids obtained by thermal cracking of waste polymers and commercial fuels with different analytical methods[J]. Talanta,2005,66 (5):1264-1271.
[25]Miskolczi N., Bartha L., Borszeki J., et al. Determination of sulfur content of diesel fuels and diesel fuel-like fractions of waste polymer cracking[J]. Talanta, 2006,69 (3):776-780.
[26]Sterry A. The influence of pressure and nitrogen compunds on the deep hydrodesulfurization of diesel fuels[J]. Revista De Chimie,2005,56 (4): 425-428.
[27]Teixeira L. S. G., Rocha R. B. S., Sobrinho E. V., et al. Simultaneous determination of copper and iron in automotive gasoline by X-ray fluorescence after pre-concentration on cellulose paper[J]. Talanta,2007,72 (3):1073-1076.
[28]Barker L. R., Kelly W. R., Guthrie W. F. Determination of sulfur in biodiesel and petroleum diesel by X-ray fluorescence (XRF) using the gravimetric standard addition method-Ⅱ[J]. Energy & Fuels,2008,22 (4):2488-2490.
[29]Zhang K. S., Hu J. N., Gao S. Z., et al. Sulfur content of gasoline and diesel fuels in northern China[J]. Energy Policy,2010,38 (6):2934-2940.
[30]He L., Kear-Padilla L., Lieberman S., et al. Rapid in situ determination of total oil concentration in water using ultraviolet fluorescence and light scattering coupled with artificial neural networks[J]. Analytica chimica acta,2003,478 (2): 245-258.
[31]Karlitschek P., Lewitzka F., B"1nting U., et al. Detection of aromatic pollutants in the environment by using UV-laser-induced fluorescence[J]. Applied Physics B:Lasers and Optics,1998,67 (4):497-504.
[32]Ozanyan K. B., Yeo T. L., Hindle F. P., et al. Fiber-based UV laser-diode fluorescence sensor for commercial gasolines[J]. Sensors Journal, IEEE,2004,4 (5):681-690.
[33]Soares I. P., Rezende T. F., Silva R. C., et al. Multivariate calibration by variable selection for blends of raw soybean oil/biodiesel from different sources using Fourier transform infrared spectroscopy (FTIR) spectra data[J]. Energy & Fuels, 2008,22 (3):2079-2083.
[34]Guarieiro L. L. N., Pinto A. C., de Aguiar P. F., et al. Determination of biodiesel percentage in biodiesel:diesel mixtures using mid-infrared spectroscopy [J]. Quimica Nova,2008,31 (2):421-426.
[35]Klingbeil A. E., Jeffries J. B., Hanson R. K. Temperature- and composition-dependent mid-infrared absorption spectrum of gas-phase gasoline: Model and measurements [J]. Fuel,2008,87 (17-18):3600-3609.
[36]Mulrooney J., Clifford J., Fitzpatrick C., et al. Detection of carbon dioxide emissions from a diesel engine using a mid-infrared optical fibre based sensor[J]. Sensors and Actuators a-Physical,2007,136 (1):104-110.
[37]Sumizawa H., Yamada H., Tonokura K. Real-time monitoring of nitric oxide in diesel exhaust gas by mid-infrared cavity ring-down spectroscopy[J]. Applied Physics B-Lasers and Optics,2010,100 (4):925-931.
[38]Kelly J. J., Barlow C. H., Jinguji T. M., et al. Prediction of gasoline octane numbers from near-infrared spectral features in the range 660-1215 nm[J]. Analytical chemistry,1989,61 (4):313-320.
[39]Kardamakis A. A., Pasadakis N. Autoregressive modeling of near-IR spectra and MLR to predict RON values of gasolines[J]. Fuel,2010,89 (1):158-161.
[40]Litani-Barzilai I., Sela I., Bulatov V., et al. On-line remote prediction of gasoline properties by combined optical methods[J]. Analytica chimica acta,1997,339 (1-2):193-199.
[41]Felicio C. C., Bras L. P., Lopes J. A., et al. Comparison of PLS algorithms in gasoline and gas oil parameter monitoring with MIR and NIR[J]. Chemometrics and intelligent laboratory systems,2005,78 (1-2):74-80.
[42]史月华,陆勇,徐光明,等.主成分回归残差神经网络校正算法用于近红外光谱快速测定汽油辛烷值[J].分析化学2001,29(1)：87-91.
[43]褚小立,田高友,袁洪福,等.小波变换结合多维偏最小二乘方法用于近红外光谱定量分析[J].分析化学,2006,34 175-178.
[44]Fernanda Pimentel M., Ribeiro G. M. G. S., da Cruz R. S., et al. Determination of biodiesel content when blended with mineral diesel fuel using infrared spectroscopy and multivariate calibration[J]. Microchemical Journal,2006,82 (2):201-206.
[45]孔翠萍,褚小立,杜泽学,等.近红外光谱方法预测生物柴油主要成分[J].分析化学,2010,38(6)：805-810.
[46]Breitkreitz M. C, Raimundo I. M., Rohwedder J. J. R., et al. Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration[J]. Analyst,2003,128(9):1204-1207.
[47]Felizardo P., Baptista P., Menezes J. C., et al. Multivariate near infrared spectroscopy models for predicting methanol and water content in biodiesel[J]. Analytica chimica acta,2007,595 (1-2):107-113.
[48]王艳斌,齐洪祥.人工神经网络用于近红外光谱测定柴油闪点[J].分析化学,2000,28(9)：1070-1073.
[49]姚肖刚,戴连奎,方骏.基于支持向量机的柴油十六烷值近红外光谱测量方法[J].化工自动化及仪表,2004,31(2)：48-51.
[50]Fernandes H. L., Ivo Jr M. R., Pasquini C., et al. Simultaneous determination of methanol and ethanol in gasoline using NIR spectroscopy:Effect of gasoline composition[J]. Talanta,2008,75 (3):804-810.
[51]Cooper J. B., Larkin C. M., Schmitigal J., et al. Rapid Analysis of Jet Fuel Using a Handheld Near-Infrared (NIR) Analyzer[J]. Applied spectroscopy,2011, 65(2):187-192.
[52]Cramer J. A., Morris R. E., Rose-Pehrsson S. L. Use of Genetic Algorithms To Improve Partial Least Squares Fuel Property and Synthetic Fuel Modeling from Near-Infrared Spectra[J]. Energy & Fuels,2010,24 5560-5572.
[53]Balabin R. M., Lomakina E. I., Safieva R. Z. Neural network (ANN) approach to biodiesel analysis:Analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy [J]. Fuel, 2011,90 (5):2007-2015.
[54]Balabin R. M., Safieva R. Z. Motor oil classification by base stock and viscosity based on near infrared (NIR) spectroscopy data[J]. Fuel,2008,87 (12): 2745-2752.
[55]Balabin R. M., Safieva R. Z. Gasoline classification by source and type based on near infrared (NIR) spectroscopy data[J]. Fuel,2008,87 (7):1096-1101.
[56]Balabin R. M., Safieva R. Z., Lomakina E. I. Gasoline classification using near infrared (NIR) spectroscopy data:Comparison of multivariate techniques[J]. Analytica chimica acta,2010,671 (1-2):27-35.
[57]Veras G., Gomes A. A., Silva A. C., et al. Classification of biodiesel using NIR spectrometry and multivariate techniques[J]. Talanta,2010,83 565-568.
[58]Xu Z. F., Bunker C. E., Harrington P. D. Classification of Jet Fuel Properties by Near-Infrared Spectroscopy Using Fuzzy Rule-Building Expert Systems and Support Vector Machines[J]. Applied spectroscopy,2010,64 (11):1251-1258.
[59]Gonzaga F. B., Pasquini C. A Low Cost Short Wave Near Infrared Spectrophotometer:Application for Determination of Quality Parameters of Diesel Fuel[J]. Analytica chimica acta,2010,670 (1-2):92-97.
[60]李淑玲.拉曼光谱仪及其应用进展[J].岩矿测试,1998,17(4)：312-316.
[61]胡军,胡继明.拉曼光谱在分析化学中的应用进展[J].分析化学,2000,28(6)：764-771.
[62]Gerrard D. L. Raman spectroscopy[J]. Analytical Chemistry,1994,66 (12): 547-557.
[63]McCreery R. L. Raman spectroscopy for chemical analysis[M]. Hobokon:John Wiley & Sons Inc 2000.
[64]Cooper J. B., Wise K. L., Groves J., et al. Determination of octane numbers and Reid Vapor pressure of commercial petroleum fuels using FT-Raman spectroscopy and partial least-squares regression analysis[J]. Analytical chemistry,1995,67 (22):4096-4100.
[65]Cooper J. B., Wise K. L., Welch W. T., et al. Determination of Weight Percent Oxygen in Commercial Gasoline:A Comparison between FT-Raman, FT-IR, and Dispersive Near-IR Spectroscopies[J]. Applied spectroscopy,1996,50 (7): 917-921.
[66]覃旭松.基于拉曼光谱的汽油辛烷值测定方法[D].浙江大学2004.
[67]包鑫,戴连奎.汽油多参数拉曼光谱分析仪中的稳健支持向量机方法[J].仪器仪表学报,2009,30(9)：1829-1835.
[68]淡图南,戴连奎.结合异常样本检测的汽油族组成拉曼光谱分析[J].光谱学与光谱分析,2010,30(4)：979-983.
[69]李晟,戴连奎.基于拉曼光谱的汽油牌号快速识别[J].光谱学与光谱分析,2010,30(11)：2993-2997.
[70]阮华,戴连奎.支持向量机分类与回归联合建模方法及其在拉曼光谱分析中的应用[J].仪器仪表学报,2010,31(11).
[71]Ku M. S., Hoeil C. Compositional analysis of naphtha by FT-Raman spectroscopy [J]. Bulletin-Korean Chemical Society,1999,20 159-162.
[72]Santos Jr V. O., Oliveira F. C. C., Lima D. G., et al. A comparative study of diesel analysis by FTIR, FTNIR and FT-Raman spectroscopy using PLS and artificial neural network analysis[J]. Analytica chimica acta,2005,547 (2): 188-196.
[73]Oliveira F. C. C., Brand o C. R. R., Ramalho H. F., et al. Adulteration of diesel/biodiesel blends by vegetable oil as determined by Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy[J]. Analytica chimica acta,2007,587 (2):194-199.
[74]Ye Q., Xu Q., Yu Y., et al. Rapid and quantitative detection of ethanol proportion in ethanol-gasoline mixtures by Raman spectroscopy[J]. Optics Communications,2009,282 (18):3785-3788.
[75]林艺玲,戴连奎,阮华.基于低分辨率色散型拉曼光谱仪的汽油苯含量快速分析[J].光谱学与光谱分析,2010,(11)：3002-3006.
[76]Stark E., Luchter K., Margoshes M. Near-infrared analysis (NIRA):A technology for quantitative and qualitative analysis[J]. Applied Spectroscopy Reviews,1986,22 (4):335-399.
[77]陆婉珍,强冬梅,徐广通,等.现代近红外光谱分析技术[M].北京：中国石化出版社,2000.
[78]Siesler H. W., Ozaki Y., Kawata S., et al. Near-infrared spectroscopy[M]. Weinheim:Wiley-VCH,2002.
[79]严衍禄.近红外光谱分析基础与应用[M].北京：中国轻工业出版社,2005.
[80]Brereton R. Chemometrics[J]. Chemistry in Britain,2000,36 (5):28-31.
[81]Brown S. D., Sum S. T., Despagne F., et al. Chemometrics[J]. Analytical chemistry,1996,68 (12):21-62.
[82]Chen Q., Zhao J., Fang C., et al. Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM)[J]. Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy,2007,66 (3):568-574.
[83]Lavine B. K. Chemometrics[J]. Analytical Chemistry,1998,70 (12):209-228.
[84]Lavine B. K. REVIEWS-Chemometrics[J]. Analytical Chemistry-Columbus, 2000,72 (12):91-97.
[85]Pasti L., Jouan-Rimbaud D., Massart D. L., et al. Application of Fourier transform to multivariatc calibration of near-infrared data[J]. Analytica chimica acta,1998,364 (1-3):253-263.
[86]Savitzky A., Golay M. J. E. Smoothing and differentiation of data by simplified least squares procedures[J]. Analytical chemistry,1964,36 (8):1627-1639.
[87]褚小立,袁洪福,陆婉珍.近红外分析中光谱预处理及波长选择方法进展与应用[J].化学进展,2004,16(4)：528-542.
[88]Barnes R., Dhanoa M., Lister S. J. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra[J]. Applied spectroscopy, 1989,43 (5):772-777.
[89]Dhanoa M., Lister S., Sanderson R., et al. The link between multiplicative scatter correction (MSC) and standard normal variate (SNV) transformations of NIR spectra[J]. Journal of Near Infrared Spectroscopy,1994,2 43-47.
[90]Wold H. Soft modeling by latent variables:the nonlinear iterative partial least squares approach[J]. Perspectives in probability and statistics, papers in honour of MS Bartlett,1975,520-540.
[91]Thomas E. V., Haaland D. M. Comparison of multivariate calibration methods for quantitative spectral analysis[J]. Analytical chemistry,1990,62 (10): 1091-1099.
[92]Thomas E. Gasohol:A green fuel[J]. Chemical engineering world,2003,38 (4): 37-38.
[93]Agarwal A. K. Biofuels (alcohols and biodiesel) applications as fuels for internal combustion engines[J]. Progress in Energy and Combustion Science, 2007,33 (3):233-271.
[94]申志兵,曹祖宾,赵金花,等.甲醇汽油使用性能的测试分析及综合评价[J].炼油技术与工程,2006,36(11)：49-51.
[95]张辉,李君,王立军,等.甲醇汽油发动机甲醛排放的测量与分析[J].吉林大学学报：工学版,2008,38(1)：32-36.
[96]吕胜春,李晖,祁东辉,等.甲醇／汽油混合燃料发动机非常规排放成分测量方法研究[J].内燃机学报,2006,24(1)：57-61.
[97]刘圣华,李晖,吕胜春,等.甲醇-汽油混合燃料对汽油机性能和排放的影响[J].西安交通大学学报,2006,40(1)：1-4.
[98]GB/T 23799-2009《车用甲醇汽油(M85)》[S].
[99]占细雄.光栅分光型乙醇汽油近红外光谱分析关键技术研究[D].吉林大学2008.
[100]淡图南,戴连奎.基于PLS投影分析的光谱波段选择方法[J].光谱学与光谱分析,2009,29(2)：351-354.
[101]Balabin R. M., Safieva R. Z., Lomakina E. I. Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction[J]. Chemometrics and intelligent laboratory systems,2007,88 (2):183-188.
[102]程丽华,吴金林.石油产品基础知识[M].北京：中国石化出版社,2006.
[103]GB/T 11132—2008《液体石油产品烃类的测定(荧光指示剂吸附法)》[S].
[104]SH/T 0741—2004《汽油中烃族组成测定法(多维气相色谱法)》)[S].
[105]褚小立,许育鹏,陆婉珍.支持向量回归建立成品汽油通用近红外校正模型的研究[J].分析测试学报,2008,27(6)：619-622.
[106]田高友,袁洪福,褚小立,等.结合小波变换与微分法改善近红外光谱分析精度[J].光谱学与光谱分析,2005,25(4)：516-520.
[107]史永刚,刘绍璞,宋世远,等.基于支持向量机的汽油族组成近红外光谱分 析方法研究[J].分析测试学报,2007,26(3)：343-346.
[108]柯以侃,董慧茹.分析化学手册,第三分册,光谱分析[M].北京：化学工业出版社,1998.