Nondestructive analytical procedure for simultaneous estimation of the major classes of hydrocarbon constituents of finished gasolines

Kelly, Jeffrey J.; Callis, James B.

doi:10.1021/ac00213a019

Cited by 102 publications

(36 citation statements)

References 61 publications

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“…23 However, the 5400-6200 cm À1 region is useful, which encompasses bands originating from the¯rst overtones of the C-H stretching modes. 24 The C-H stretching overtones in the 5400-6200 cm À1 region contribute substantially enough to form the basis for quantitative analysis of the four active components. 25,26 Therefore, for the total°avonoids, puerarin, salvianolic acid B and icariin models, the \¯ngerprint region" (4500-5000 cm À1 ), the \water regions" (5000-5400 cm À1 and 6200-7500 cm À1 ) 27 and the region with low intensity and low signal-tonoise ratio (7500-10000 cm À1 ) were excluded, and the spectral region actually employed was 5400-6200 cm À1 (see Fig.…”

Section: Nir Spectral Analysismentioning

confidence: 99%

Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy

Lou

Yang

Zhu

et al. 2014

J. Innov. Opt. Health Sci.

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A particle swarm optimization (PSO)-based least square support vector machine (LS-SVM) method was investigated for quantitative analysis of extraction solution of Yangxinshi tablet using near infrared (NIR) spectroscopy. The usable spectral region (5400-6200 cm À1 ) was identi¯ed, then the¯rst derivative spectra smoothed using a Savitzky-Golay¯lter were employed to establish calibration models. The PSO algorithm was applied to select the LS-SVM hyperparameters (including the regularization and kernel parameters). The calibration models of total°a vonoids, puerarin, salvianolic acid B and icariin were established using the optimum hyperparameters of LS-SVM. The performance of LS-SVM models were compared with partial least squares (PLS) regression, feed-forward back-propagation network (BPANN) and support vector machine (SVM). Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS-SVM method were superior to PLS, BP-ANN and SVM. For PSObased LS-SVM models, the determination coe±cients (R 2 ) for the calibration set were above 0.9881, and the RSEP values were controlled within 5.772%. For the validation set, the RMSEP values were close to RMSEC and less than 0.042, the RSEP values were under 8.778%, which This is an Open Access article published by World Scienti¯c Publishing Company. It is distributed under the terms of the Creative Commons Attribution 3.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited. Vol. 7, No. 6 (2014) were much lower than the PLS, BP-ANN and SVM models. The PSO-based LS-SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy, which has de¯nite practice signi¯cance and application value. Journal of Innovative Optical Health Sciences

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Section: Nir Spectral Analysismentioning

confidence: 99%

Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy

Lou

Yang

Zhu

et al. 2014

J. Innov. Opt. Health Sci.

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“…The short-wave NIR *Address correspondence to this author at the 104 Staff Room, Naval Aeronautical Engineering Institute, Yantai, Shandong Postcard: 264001, China; Tel: 0535-6635574-8006; E-mail: zhnxing2008@163.com (SW-NIR) situated in region between 700-1,100nm has greatly decreased absorptivities but has the distinct advantages that it can be transmitted over inexpensive fiber optics, can utilize long path lengths (1-5cm), and can be measured with very inexpensive light sources (tungsten lamps) and detectors (silicon diode array or silicon charger coupled device, shorted for CCD). There are already some documents involved SW-NIR application [10,11].…”

Section: Introductionmentioning

confidence: 99%

Short-Wave Near-Infrared Spectroscopy for Rapid Quantification of Acidity of Aviation Kerosene

Xing¹,

Wang²,

Shen³

2008

TOEFJ

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The application of short-wave near-infrared spectroscopy (SW-NIRS) in acidity of aviation kerosene was described on the term of chemometrics. This spectral region is interesting because this type of spectrometer reduces costs in comparison with traditional long-wave near-infrared (LW-NIR) instrument. The SW-NIR calibration models of aviation kerosene were established by partial least square regression (PLS). Satisfactory calibration statistics were obtained with a standard error of calibration (SEC) of 0.028 mgKOH/100mL and a standard error of prediction (SEP) of 0.036 mgKOH/100mL. A t-test showed that the NIRS model had an accuracy equivalent to that of GB/T 264-91, which was equal to ASTM D 847, in the analysis of acidity of aviation kerosene. The NIRS method had the advantages of rapid analysis, better repeatability, little simple, and online analysis, etc.

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“…The spectral features of gasoline are dominated by overtone and combination bands of CH, CH 2 , and CH 3 , groups, and the most useful spectral information is located in the 1100-1650 nm and 1800-2100 nm spectral ranges (Hoeil, Hyeseon, and Chi Hyuck, 2001). There have been a number of reports in the past years about the prediction of octane number of gasoline using spectroscopic techniques (Kelly and Callis, 1990;Fodor, Kohl, and Mason, 1996;Litani-Barzilai et al, 1997;Kalivas, 1997;Brenchley, Hörchner, and Kalivas, 1997). However, the conventional method used to determine the octane number in the petroleum industry is the knocking engine, which requires long analysis times and is not suitable for online monitoring.…”

Section: Introductionmentioning

confidence: 99%

Determination of Octane Number of Gasoline Using Near Infrared Spectroscopy and Genetic Multivariate Calibration Methods

Özdemir

2005

Petroleum Science and Technology

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Abstract:The feasibility of rating the octane number of gasoline using near infrared (NIR) spectroscopy and three different genetic algorithm-based multivariate calibration methods was demonstrated. The three genetic multivariate calibration methods are genetic regression (GR), genetic classical least squares (GCLS), and genetic inverse least squares (GILS). The sample data set was obtained from the ftp address (ftp://ftp.clarkson.edu/pub/hopkepk/Chemdata/) with the permission of Professor. J. H. Kalivas. This data set contains the NIR spectra of 60 gasoline samples collected using diffuse reflectance as log (1/R) with known octane numbers and covers the range from 900 to 1700 nm in 2 nm intervals. Of these 60 spectra, 20 were used as the calibration set, 20 were used as the prediction set, and 20 were reserved for the validation purposes. Several calibration models were built with the three genetic algorithm-based methods, and the results were compared with the partial least squares (PLS) prediction errors reported in the literature. Overall, the standard error of calibration (SEC), standard error of prediction (SEP), and standard error of validation (SEV) values were in the range of 0.15-0.32 (in the units of motor octane number) for the GR and GILS, which are comparable with the literature. However, GCLS produced relatively large results (0.36 for SEC, 0.39 for SEP and 0.52 for SEV) when compared with the other two methods.

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Nondestructive analytical procedure for simultaneous estimation of the major classes of hydrocarbon constituents of finished gasolines

Cited by 102 publications

References 61 publications

Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy

Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy

Short-Wave Near-Infrared Spectroscopy for Rapid Quantification of Acidity of Aviation Kerosene

Determination of Octane Number of Gasoline Using Near Infrared Spectroscopy and Genetic Multivariate Calibration Methods

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