基于支持向量机的汽油族组成近红外光谱分析方法研究

提出一种基于最小二乘支持向量机(LS-SVM)的汽油族组成近红外光谱分析方法。采用国家标准方法(GB 11132-1989液体石油产品烃类测定法——荧光指示剂吸附法)测定了重庆地区销售的汽油族组成,并采用主成分分析-最小二乘支持向量机建立汽油族组成的预测模型。预测模型对汽油中芳烃和烯烃含量的RMSEC分别为0.2090和0.2142。实验结果表明所建模型具有计算量小,预测准确、可靠,而且操作简单、维护费及测试费用低等特点。     

判别分析用于烃类化合物分类及汽油样品的族组成分析

研究了判别分析用于烃类化合物分类的可行性，以198个烃类化合物在BD－1和BD－5固定相上同一柱温下的保留指数差及其在各柱上的温度系数为判别变量，成功地建立了烷烃、环烷烃、芳烃和烯烃的判别函数，以此对裂化汽油，重整生成汽油、石脑油和蒸馏常顶汽油进行了族组成分析，经气相色谱－质谱分析验证了结果的正确性，为汽油产品的族组成分析提供了一种新的方法。     

全二维气相色谱法用于不同石油馏分的族组成分布研究

一直以来采用化学法、色谱法或质谱法等多种方法联用分析不同石油馏分中一 些目标化合物和芳烃含量，但这些方法操作繁琐，迫切需要改进。作者采用全二维 气相色谱方法（GC×GC）研究不同沸程范围的蒸馏汽油、煤油柴油和裂化柴油的烷 烃（P）、烯烃＋环烷烃（O＋N）和1－4环芳烃（A）的族分离新方法。经过对柱系 统进行选择和对色谱条件进行优化，一个GC×GC方法即可实现对不同石油馏分的族 组成分离和目标化合物分离。用标准物对油品中一些特征组分进行定性，并对特征 组分和不同沸程的石油馏分的P，（O＋N），A族组成进行定量和比较，定量结果的 相对标准偏差（RSD）≤2.3。一个GC×GC方法便可完成原来要几个美国测试和材料 协会（ASTM）方法才能完成的任务。     

毛细管气相色谱测定改进费-托法合成汽油辛烷值的研究

采用高分辨毛细管气相色谱法对改进费－ 托（ ＭＦＴ） 法合成汽油馏分进行了组成分析, 按其化学类型和各组分的辛烷值特性分组, 并与标准方法测定的辛烷值进行关联和线性回归分析, 获得了每组的有效辛烷值及计算ＭＦＴ合成汽油辛烷值的研究法辛烷值（ＲＯＮ） 和马达法辛烷值（ ＭＯＮ） 方程。该法的建立, 对正在研究开发中的ＭＦＴ合成汽油的工艺、动力学研究、催化剂评价等具有现实意义。     

火焰原子吸收光谱法快速测定汽油中的铅

１　引　言四乙基铅是汽油中常用的抗爆剂。由于铅会造成严重的环境污染，近年来，四乙基铅在汽油中的使用日益受到严格限制，所以建立测定汽油中铅的简便的分析方法是非常重要的。现有测定汽油中铅的方法是容量分析法，需要经过萃取、蒸发、氧化、溶解等步骤处理油样，较为繁琐。本文使汽油与表面活性剂聚乙二醇辛基苯基醚（ＯＰ）、正丁醇和水形成微乳液后直接进样进行火焰原子吸收测定，该法具有简单、快速、准确的特点。２　实验部分２．１　主要仪器及试剂　ＷＹＸ－４０１型原子吸收分光光度计（沈阳分析仪器厂），铅空心阴极灯，铅标…     

气相色谱分析汽油族组成

汽油的族组成分析,已有的标准方法多采用液体吸附色谱法,气相色谱分析汽油族组成已有一些报道,但烯烃的定量方法仍较复杂,精确度也差。1981年Ury提出采用硫酸铜吸附烯烃并在高温下脱附,以对汽油中烯烃定量可以简化操作手续,并保证     

毛细管气相色谱法同库仑法联合测定含烯汽油的烃族及芳烃组成

本文在使用填充柱色谱法与库仑法联合测定含烯汽油烃族组成的基础上,又使用涂以极性固定液的FFAP WCOT毛细柱分析了含烯汽油的饱和烃/烯烃及芳烃、芳烃单体烃的含量。通过芳烃单体烃的测定确定油品的碳数及准确扣除高沸点非芳烃组分在芳烃部分中的含量,使定量更加准确可靠。     

近红外光谱法快速测定乙烯原料油族组成

采用色谱仪测定乙烯原料油族训练集样品的组成性质的基础数据与近红外光谱仪测定训练集样品的近红外光谱,建立了乙烯原料油族基础数据和近红外光谱库,用化学计量学分析软件,采用偏最小二乘（PLS）方法将训练集样品的光谱和相应已测定样品的族组成基础数据相关联,建立了乙烯原料中正构烷烃、异构烷烃、环烷烃、芳烃、烯烃的定量和定性分析模型。该模型具有测定结果准确、可靠,测定简便、讯速的优点,适合在线生产中对样品的快速检测。     

全二维气相色谱-飞行时间质谱对催化裂化汽油的定性与定量分析

采用全二维气相色谱-飞行时间质谱(GC×GC-TOF MS)对催化裂化汽油全馏分进行了定性与定量分析,建立了相应的分析方法.结果表明,汽油族组成中的烷烃、烯烃、环烷烃、芳烃在全二维点阵谱图中呈分区域、带状的分布特点.GC×GC-TOF MS根据催化裂化汽油组分内分子的沸点及极性差异对其进行两个维度分离,极大地避免了普通色谱法分析过程中沸点相似化合物共流的弊端,实现催化裂化汽油组分的精确分离和准确定性分析.通过引入响应因子,修正了不同性质的烃类在电离源上电离效率的差异,使得TOF对催化裂化汽油族组成的定量结果与普通气相色谱法的定量结果的相关性较好,且应用GC×GC-TOF MS方法获得了催化裂化汽油更为精确的族组成信息.GC×GC-TOF MS为催化裂化汽油精确表征提供了一种有效方法.     

重油催化裂化汽油中酚类化合物的分离分析

利用100g/L NaOH作萃取剂，二氯甲烷为反萃取剂，油剂比为5：1（体积比）时浓缩分离石家庄炼油厂和燕山石油化工公司的重油催化裂化（RFCC）稳定汽油和预碱洗后汽油中的酚类化合物；多面手利用色谱-质谱联用法对浓缩分离出的酚类化合物进行了检测，结果发现RFCC汽油中的酚类化合物主要是C0-C3苯酚，还有少量C4苯酚；所用稳定汽油和预碱洗后汽油的酚类组成相似，但碱洗后苯酚含量减少。     

用近红外光谱仪快速测定调和汽油中芳烃、烯烃的含量

介绍了带有CCD检测器的近红光普仪在调和汽油分析中的应用，并与标准测定方法测定的数据对比，验证了近红外光谱法测定汽油性质的可靠性，同时表明该方法具有分析速度快，重现性好等特点。     

Determination of Quality Properties of Soy Sauce by Support Vector Regression Coupled with SW-NIR Spectroscopy

The modern near-infrared(NIR) spectroscopy analysis is a simple, efficient and nondestructive technique, which has been used in chemical analysis in diverse fields. Shortwave NIR spectroscopy is also a rapid, flexible, and cost-effective method to control product quality in food industry. The method of support vector regression coupled with shortwave NIR spectroscopy was explored for the nondestructive quantitative analysis of the important quality parameters of soy sauce, including amino nitrogen content, ...     

复合近红外光谱法在牛奶测定中的应用

研究了用于快速测定牛奶主要成分含量的近红外透射光谱法和漫反射光谱法,分析了两种方法在实际测量中的差异,并提出将两种方法结合形成复合光谱法用于牛奶成分含量的检测。结果表明:复合光谱法的应用有效地提高了多变量回归分析中牛奶成分含量的预测精度。     

基于近红外光谱技术的亚麻纤维化学成分含量快速测定

在推进亚麻纤维的纺纱及其产业化生产过程中,快速、准确的定量分析纤维的化学成分是重要趋势。该研究利用近红外光谱技术分析亚麻纤维化学成分,以化学分析法测定值为对照,采用偏最小二乘法(PLS)建立亚麻纤维化学成分的近红外模型,从而实现了其化学成分的高效、快速定量分析。结果表明,建立的亚麻纤维纤维素、半纤维素、木质素和果胶近红外模型的校正相关系数(R_C)与验证相关系数(R_(CV))均在0.9以上,校正均方根误差(RMSEC)小于预测均方根误差(RMSEP)且均小于1。外部验证和双尾t检验表明模型预测结果较为准确,预测值与化学分析法得到的实测值无显著性差异,故该模型可用于相关化学成分含量的快速预测。     

Screening Brazilian commercial gasoline quality by hydrogen nuclear magnetic resonance spectroscopic fingerprintings and pattern-recognition multivariate chemometric analysis

The identification of gasoline adulteration by organic solvents is not an easy task, because compounds that constitute the solvents are already in gasoline composition. In this work, the combination of Hydrogen Nuclear Magnetic Resonance (¹H NMR) spectroscopic fingerprintings with pattern-recognition multivariate Soft Independent Modeling of Class Analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a Monitoring Program for Quality Control of Automotive Fuels. SIMCA was performed on spectroscopic fingerprints to classify the quality of representative commercial gasoline samples selected by Hierarchical Cluster Analysis (HCA) and collected over a 6-month period from different gas stations in the São Paulo state, Brazil. Following optimized the ¹H NMR-SIMCA algorithm, it was possible to correctly classify 92.0% of commercial gasoline samples, which is considered acceptable. The chemometric method is recommended for routine applications in Quality-Control Monitoring Programs, since its measurements are fast and can be easily automated. Also, police laboratories could employ this method for rapid screening analysis to discourage adulteration practices.     

近红外光谱法快速测定聚乙烯醇的醇解度

应用傅里叶变换近红外漫反射光谱技术,通过对光谱预处理方法的选择和模型优化,建立了准确度较好的聚乙烯醇醇解度近红外定量分析模型,并验证了其可行性。通过验证试验表明,其预测值接近于化学法测定值,适合聚乙烯醇醇解度快速测定。     

GC Fingerprints Coupled to Pattern-Recognition Multivariate SIMCA Chemometric Analysis for Brazilian Gasoline Quality Studies

ASTM D6729 gas chromatographic fingerprinting coupled to pattern-recognition multivariate soft independent modeling of class analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality. SIMCA, was performed on gas chromatographic fingerprints to classify the quality of representative commercial gasoline samples selected by hierarchical cluster analysis and collected over a 5 month period from gas stations in São Paulo State, Brazil. Following an optimized ASTM D6729 gas chromatographic-SIMCA algorithm, it was possible to correctly classify the majority of commercial gasoline samples. The method could be employed for rapid monitoring to discourage adulteration.     

Discrimination of Ganoderma lucidum according to geographical origin with near infrared diffuse reflectance spectroscopy and pattern recognition techniques

A rapid and nondestructive near infrared (NIR) method combined with chemometrics was used to discriminate Ganoderma lucidum according to cultivation area. Raw, first, and second derivative NIR spectra were compared to develop a robust classification rule. The chemical properties of G. lucidum samples were also investigated to find out the difference between samples from six varied origins. It could be found that the amount of polysaccharides and triterpenoid saponins in G. lucidum samples was considerably different based on cultivation area. These differences make NIR spectroscopic method viable. Principal component analysis (PCA), discriminant partial least-squares (DPLS) and discriminant analysis (DA) were applied to classify the geographical origins of those samples. The results showed that excellent classification could be obtained after optimizing spectral pre-treatment. For the discriminating of samples from three different provinces, DPLS provided 100% correct classifications. Moreover, for samples from six different locations, the correct classifications of the calibration as well as the validation data set were 96.6% using the DA method after the SNV first derivative spectral pre-treatment. Overall, NIR diffuse reflectance spectroscopy using pattern recognition was shown to have significant potential as a rapid and accurate method for the identification of herbal medicines.     

Screening Brazilian Commercial Gasoline Quality by ASTM D6733 GC and Pattern-Recognition Multivariate SIMCA Chemometric Analysis

The combination of ASTM D6733 gas chromatographic fingerprinting data with pattern-recognition multivariate soft independent modeling of class analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a monitoring program for quality control of automotive fuels. SIMCA was performed on chromatographic fingerprints to classify the quality of the gasoline samples. Using SIMCA, it was possible to correctly classify 94.0% of commercial gasoline samples, which is considered acceptable. The method is recommended for quality-control monitoring. Quality control and police laboratories could employ this method for rapid monitoring.     

基于傅立叶变换的人工神经网络近红外光谱定量分析法

将原始光谱进行一定的预处理后,以其快速傅立叶变换FFT的前N个系数作为人工神经网络(ANN)的输入量,不仅确保了大量有用信息参与模型的建立,同时实现了优越的滤波功能。以汽油的辛烷值和煤粉干燥基高位发热量(Qgr.d)的近红外光谱建模,当采用前20个FFT系数的傅立叶变换-径向基网络(FFT-RBF)时,辛烷值模型的预测误差均方根(RMSEP)可达0.152,相关系数为0.976,当采用前30个FFT系数时,快速FFT-RBF煤粉干燥基高位发热量模型的RMSEP为0.256,相关系数为0.923,说明FFT-RBF模型有着很好的预测能力。研究表明基于傅立叶变换的人工神经网络近红外光谱定量分析法,特别是FFT-RBF具有良好的预测能力。