Determination of motor gasoline adulteration using FTIR spectroscopy and multivariate calibration

Al-Ghouti, Mohammad A.; Al‐Degs, Yahya S.; Amer, Mohammad W.

doi:10.1016/j.talanta.2008.05.024

Cited by 77 publications

(51 citation statements)

References 15 publications

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“…However, in many real applications the (FTIR) spectrum of oil mixtures, foods, drug formulations, and fuels is very complicated and running simple univariate calibration on single wavenumber/wavelength is rather limited . Extraction of useful analytical information from complicated and overlapped (FTIR) spectra is possible if a suitable multivariate calibration tool is applied (Al-Ghouti et al 2008. In our laboratories, we have managed to apply multivariate calibration with (FTIR) and UV/Vis spectroscopy for detecting fuel adulteration (Al-Ghouti et al 2008), determination of viscosity index and base number of motor oils , determination of pesticide in groundwater (Al-Degs 2009), and determination of hydrogen content, gross heat of combustion, and net heat of combustion of diesel fuel (Al-Ghouti and Al-Degs 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Extraction of useful analytical information from complicated and overlapped (FTIR) spectra is possible if a suitable multivariate calibration tool is applied (Al-Ghouti et al 2008. In our laboratories, we have managed to apply multivariate calibration with (FTIR) and UV/Vis spectroscopy for detecting fuel adulteration (Al-Ghouti et al 2008), determination of viscosity index and base number of motor oils , determination of pesticide in groundwater (Al-Degs 2009), and determination of hydrogen content, gross heat of combustion, and net heat of combustion of diesel fuel (Al-Ghouti and Al-Degs 2010). In the earlier research, the expensive and lengthy standard analytical methods were replaced by simple and lessexpensive (FTIR) methods.…”

Section: Introductionmentioning

confidence: 99%

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Determination of Frying Quality of Vegetable Oils used for Preparing Falafel using Infrared Spectroscopy and Multivariate Calibration

2011

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The frying qualities of palm and soybean oils are determined using infrared spectroscopy and multivariate calibration. Compare to soybean oil, palm oil is more resistive to the chemical and physical changes and this is attributed to the high degree of unsaturation of soybean oil (61.9%) compare to palm oil (13.8%). After 48 h in service, the oil samples were effectively clustered into two groups using principal component analysis which indicated that both oils still maintain their chemical identities. Partial least squares regression (PLS1 and PLS2) a long with mid-FTIR data are used for predicting free fatty acid, viscosity, and total polar compounds of the used oils without running expensive standard procedures. PLS1 and PLS2 outperformed PCR and MLR for predicting the quality indicators of the frying oils. For palm oil and at the optimum calibration conditions, the obtained accuracies (SD) are 105.6% (0.05), 99.8% (1.10), and 103.9% (0.16) for free fatty acid, viscosity, and total polar compounds, respectively. The proposed method is simple, less-expensive, and has comparable accuracy/precision with standard procedures that often used for monitoring frying oils.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determination of Frying Quality of Vegetable Oils used for Preparing Falafel using Infrared Spectroscopy and Multivariate Calibration

2011

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“…The composition of MOGAS can reach hundreds of different natural components (such as benzene and toluene) and also some additives such as Methyl Tertiary Butyl Ether (MTBE) [1] . The composition of the carbon atoms in the hydrocarbon-forming MOGAS between 4-12, with a range of distillate ranging from 30-225° C at atmospheric pressure [2] .…”

Section: Introductionmentioning

confidence: 99%

Optimizing an Octane Number of Motor Gasoline (MOGAS) 91 RON with Blending Methods

Huda

Wakimin

Sarungu

2017

Indonesian J Appl Phys

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Motor Gasoline (MOGAS) that serves as modifiers thermal energy into mechanical energy with a Research Octane Number (RON) of 91 was produced by mixing (blending) of two components. RON are high is one of MOGAS specifications needed for good engine performance. To obtain a MOGAS with high RON usually needs additional materials, chemical components. In this work, we don't need any additional materials to increase the RON. In the field of oil and gas blending method is a method that is widely used to acquire new products that are superior and achieve quality standards. The components used in blending these are products with a value of RON 88 and 92. Examination of specific gravity (ASTM D1298) was conducted to obtain specific gravity (SG) and temperature observations (T), while the ASTM D86 distillation examination was conducted to determine the components in materials. The results showed that the product specifications in accordance with the existing products on the market. The acquisition of distillate reached 95% with losses of about 3.9%, and the residue of 1.1%.

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“…Determination of motor gasoline adulteration using FTIR spectroscopy and multivariate calibration was studied [9] using a practical procedure based on the use of density, distillation temperatures, and FTIR analysis along with multivariate calibration. Thirteen peaks of the absorbance at wavenumbers 434, 461, 484, 673, 694, 1030, 1086, 1217, 1231, 1460, 1497, 1606, and 3028 cm −1 were chosen to perform the multivariate calibration.…”

Section: Introductionmentioning

confidence: 99%

QSPR Models for Octane Number Prediction

Al‐Fahemi

Albis

Gad

2014

Journal of Theoretical Chemistry

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Quantitative structure-property relationship (QSPR) is performed as a means to predict octane number of hydrocarbons via correlating properties to parameters calculated from molecular structure; such parameters are molecular mass , hydration energy , boiling point , octanol/water distribution coefficient log , molar refractivity , critical pressure , critical volume , and critical temperature . Principal component analysis (PCA) and multiple linear regression technique (MLR) were performed to examine the relationship between multiple variables of the above parameters and the octane number of hydrocarbons. The results of PCA explain the interrelationships between octane number and different variables. Correlation coefficients were calculated using M.S. Excel to examine the relationship between multiple variables of the above parameters and the octane number of hydrocarbons. The data set was split into training of 40 hydrocarbons and validation set of 25 hydrocarbons. The linear relationship between the selected descriptors and the octane number has coefficient of determination ( 2 = 0.932), statistical significance ( = 53.21), and standard errors ( = 7.7). The obtained QSPR model was applied on the validation set of octane number for hydrocarbons giving 2 = 0.942 and = 6.328.

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Determination of motor gasoline adulteration using FTIR spectroscopy and multivariate calibration

Cited by 77 publications

References 15 publications

Determination of Frying Quality of Vegetable Oils used for Preparing Falafel using Infrared Spectroscopy and Multivariate Calibration

Determination of Frying Quality of Vegetable Oils used for Preparing Falafel using Infrared Spectroscopy and Multivariate Calibration

Optimizing an Octane Number of Motor Gasoline (MOGAS) 91 RON with Blending Methods

QSPR Models for Octane Number Prediction

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