Detection and estimation of Super premium 95 gasoline adulteration with Premium 91 gasoline using new NIR spectroscopy combined with multivariate methods

Abstract Super premium 95 octane gasoline is a special blend of petrol with a higher octane rating that can produce higher engine power, as well as knock-free performance for cars with a high-octane requirement. Super premium grade gasoline 95 is often adulterated with cheaper Premium grade 91 that lowers the octane number of the Super premium gasoline. In the present study a new Near Infrared (NIR) spectroscopy combined with multivariate analysis was developed to detect as well as to quantify the level of Premium 91 gasoline adulteration in Super premium 95 octane gasolines. In this study standard samples of Premium 91 and Super premium 95 octane gasoline were collected from Oman Oil Refineries and Petroleum Industries Company SAOC (ORPIC) and were investigated. Super premium 95 samples were then adulterated with eighteen different percentage levels: 0%, 1%, 3%, 5%, 7%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, and 75% of Premium 91 gasoline. All samples were measured using NIR spectroscopy in absorption mode in the wavelength range from 700 to 2500 nm. The multivariate methods like PCA, PLSDA and PLS regression were applied for statistical analysis of the obtained NIR spectral data. Partial least-squares discriminant analysis (PLSDA) was used to check the discrimination between the pure and adulterated gasoline samples. For PLSDA model the R-square value obtained was 0.99 with 0.012 RMSE. Furthermore, PLS regression model was also built to quantify the levels of Premium 91 adulterant in Super Premium 95 gasoline samples. The PLS regression model was obtained with the R-square 0.99 and with 1.33 RMSECV value having good prediction with RMSEP value 1.35 and correlation of 0.99. This newly developed method is having lower limit of detection less than 1.5% level for Premium 91 adulteration. It was desirable to have simple, rapid and sensitive methods to detect the presence of one petroleum product in another.

[1]  Roman M. Balabin,et al.  Gasoline classification using near infrared (NIR) spectroscopy data: comparison of multivariate techniques. , 2010, Analytica chimica acta.

[2]  M. Monteiro,et al.  Study of Brazilian Gasoline Quality Using Hydrogen Nuclear Magnetic Resonance (1H NMR) Spectroscopy and Chemometrics , 2009 .

[3]  Selmo Q. Almeida,et al.  Multivariate calibration in Fourier transform infrared spectrometry as a tool to detect adulterations in Brazilian gasoline , 2008 .

[4]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[5]  M. Al‐Ghouti,et al.  Determination of motor gasoline adulteration using FTIR spectroscopy and multivariate calibration. , 2008, Talanta.

[6]  R. Boqué,et al.  Development of new NIR-spectroscopy method combined with multivariate analysis for detection of adulteration in camel milk with goat milk. , 2017, Food chemistry.

[7]  L. d'Avila,et al.  Automotive Gasoline Quality Analysis by Gas Chromatography: Study of Adulteration , 2003, Chromatographia.

[8]  R. V. P. Rezende,et al.  Influence of solvent addition on the physicochemical properties of Brazilian gasoline , 2008 .

[9]  Roman M. Balabin,et al.  Gasoline classification by source and type based on near infrared (NIR) spectroscopy data , 2008 .

[10]  Luiz Antonio d'Avila,et al.  Adulteration detection of Brazilian gasoline samples by statistical analysis , 2005 .

[11]  Roman M. Balabin,et al.  Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction , 2007 .

[12]  E C Alexopoulos,et al.  Introduction to multivariate regression analysis. , 2010, Hippokratia.

[13]  N. Boralle,et al.  Screening Brazilian commercial gasoline quality by hydrogen nuclear magnetic resonance spectroscopic fingerprintings and pattern-recognition multivariate chemometric analysis. , 2010, Talanta.

[14]  Roman M. Balabin,et al.  Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra , 2008 .

[15]  Paulo Jorge Sanches Barbeira USING STATISTICAL TOOLS TO DETECT GASOLINE ADULTERATION , 2002 .