Skip to main content

Advertisement

Log in

Advances in chemometric control of commercial diesel adulteration by kerosene using IR spectroscopy

  • Research Paper
  • Published:
Analytical and Bioanalytical Chemistry Aims and scope Submit manuscript

Abstract

Adulteration is a recurrent issue found in fuel screening. Commercial diesel contamination by kerosene is highly difficult to be detected via physicochemical methods applied in market. Although the contamination may affect diesel quality and storage stability, there is a lack of efficient methodologies for this evaluation. This paper assessed the use of IR spectroscopies (MIR and NIR) coupled with partial least squares (PLS) regression, support vector machine regression (SVR), and multivariate curve resolution with alternating least squares (MCR-ALS) calibration models for quantifying and identifying the presence of kerosene adulterant in commercial diesel. Moreover, principal component analysis (PCA), successive projections algorithm (SPA), and genetic algorithm (GA) tools coupled to linear discriminant analysis were used to observe the degradation behavior of 60 samples of pure and kerosene-added diesel fuel in different concentrations over 60 days of storage. Physicochemical properties of commercial diesel with 15% kerosene remained within conformity with Brazilian screening specifications; in addition, specified tests were not able to identify changes in the blends’ performance over time. By using multivariate classification, the samples of pure and contaminated fuel were accurately classified by aging level into two well-defined groups, and some spectral features related to fuel degradation products were detected. PLS and SVR were accurate to quantify kerosene in the 2.5–40% (v/v) range, reaching RMSEC < 2.59% and RMSEP < 5.56%, with high correlation between real and predicted concentrations. MCR-ALS with correlation constraint was able to identify and recover the spectral profile of commercial diesel and kerosene adulterant from the IR spectra of contaminated blends.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Scheme 1
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Obeidat SM, Al-Ktash MM, Al-Momani IF. Study of fuel assessment and adulteration using EEMF and multiway PCA. Energy Fuels. 2014. CrossRef, Google Scholar. https://doi.org/10.1021/ef500718e.

  2. Krakowska B, Stanimirova I, Orzel J, Daszykowski M, Grabowski I, Zaleszczyk G, et al. Detection of discoloration in diesel fuel based on gas chromatographic fingerprints. Anal Bioanal Chem. 2015; https://doi.org/10.1007/s00216-014-8332-4. CrossRef, Google Scholar.

  3. Agência Nacional do Petróleo, Gás Natural e Biocombustíveis – ANP. Resolução No. 3 de 08.02.2007. In: DOU 09.02.2007. http://legislacao.anp.gov.br/?path=legislacao-anp/resol-anp/2007/fevereiro&item=ranp-3%2D%2D2007&export=pdf. Accessed in 20 Oct 2018.

  4. Menezes EW, Silva R, Cataluña R, Ortega RJC. Effect of ethers and ether/ethanol additives on the physicochemical properties of diesel fuel and on engine tests. Fuel. 2006; https://doi.org/10.1016/j.fuel.2005.08.027 [CrossRef] [Google Scholar].

  5. Agência Nacional do Petróleo, Gás Natural e Biocombustíveis – ANP. Resolução No. 30 de 23.06.2016. In: DOU 24.06.2016. http://www.lex.com.br/legis_27160107_RESOLUCAO_N_30_DE_23_DE_JUNHO_DE_2016.aspx. Accessed in 20 Oct 2018.

  6. Câmara ABF, de Carvalho LS, Morais CLM, Lima LAS, Araújo HOM, Oliveira FM, Lima KMG. MCR-ALS and PLS coupled to NIR/MIR spectroscopies for quantification and identification of adulterant in biodiesel-diesel blends. Fuel. 2017; https://doi.org/10.1016/j.fuel.2017.08.072 [CrossRef] [Google Scholar].

  7. Cunha IBS, Fernandes AMAP, Tega DU, Simas RC, Nascimento HL, Sá GF, et al. Quantitation and quality control of biodiesel/petrodiesel (Bn) blends by easy ambient sonic-spray ionization mass spectrometry. Energy Fuels. 2012; https://doi.org/10.1021/ef3010866. CrossRef, Google Scholar.

  8. Gotor R, Tiebe C, Schilischka J, Bell J, Rurack K. Detection of adulterated diesel using fluorescent test strips and smartphone readout. Energy Fuels. 2017; https://doi.org/10.1021/acs.energyfuels.7b01538. CrossRef, Google Scholar.

  9. Pedroso MP, Godoy LAF, Ferreira EC, Poppi RJ, Augusto F. Identification of gasoline adulteration using comprehensive two-dimensional gas chromatography combined to multivariate data processing. J Cromatogr A. 2008; https://doi.org/10.1016/j.chroma.2008.05.092. CrossRef, Google Scholar.

  10. Jose TK, Anand K. Effects of biodiesel composition on its long term storage stability. Fuel. 2016; https://doi.org/10.1016/j.fuel.2016.03.007 [CrossRef] [Google Scholar].

  11. Brereton RG, Jansen J, Lopes J, Marini F, Pomerantsev A, Rodionova O, et al. Chemometrics in analytical chemistry – part II: modeling, validation, and applications. Anal Bioanal Chem. 2018; https://doi.org/10.1007/s00216-018-1283-4. CrossRef, Google Scholar.

  12. Zhang J, Wei X, Huang J, Lin H, Deng K, Li Z, et al. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectral prediction of postmortem interval from vitreous humor samples. Anal Bioanal Chem. 2018; https://doi.org/10.1007/s00216-018-1367-1. CrossRef, Google Scholar.

  13. Aboualizadeh E, Ranji M, Sorenson CM, Sepehr R, Sheibani N, Hirschmugl CJ. Retinal oxidative stress at the onset of diabetes determined by synchrotron FTIR widefield imaging: towards diabetes pathogenesis. Analyst. 2017; https://doi.org/10.1039/c6an02603f [CrossRef] [Google Scholar].

  14. Theophilou G, Morais CLM, Halliwell DE, Lima KMG, Drury J, Martin-Hirsch PL, et al. Synchrotron- and focal plane array-based Fourier-transform infrared spectroscopy differentiates the basalis and functionalis epithelial endometrial regions and identifies putative stem cell regions of human endometrial glands. Anal Bioanal Chem. 2018; https://doi.org/10.1007/s00216-018-1111-x. CrossRef, Google Scholar.

  15. Marques AS, Moraes EP, Júnior MAA, Moura AD, Neto VFA, Neto RM, Lima KMG. Rapid discrimination of Klebsiella pneumoniae carbapenemase 2 – producing and non-producing Klebsiella pneumoniae strains using near-infrared spectroscopy (NIRS) and multivariate analysis. Talanta. 2015; https://doi.org/10.1016/j.talanta.2014.11.006 [CrossRef] [Google Scholar].

  16. Hu J, Ma X, Liu L, Wu Y, Ouyang J. Rapid evaluation of the quality of chestnuts using near-infrared reflectance spectroscopy. Food Chem. 2017; https://doi.org/10.1016/j.foodchem.2017.03.127 [CrossRef] [Google Scholar].

  17. Corgozinho CNC, Pasa VMD, Barbeira PJS. Determination of residual oil in diesel oil by spectrofluorimetric and chemometric analysis. Talanta. 2008; https://doi.org/10.1016/j.talanta.2008.03.003 [CrossRef] [Google Scholar].

  18. Thissen U, Pepers M, Ustun B, Melssen WJ, Buydens LMC. Comparing support vector machines to PLS for spectral regression applications. Chem Intell Lab Syst. 2004; https://doi.org/10.1016/j.chemolab.2004.01.002 [CrossRef] [Google Scholar].

  19. Dantas WFC, Alves JCL, Poppi RJ. MCR-ALS with correlation constraint and Raman spectroscopy for identification and quantification of biofuels and adulterants in petroleum diesel. Chemom Intell Lab Syst. 2017; https://doi.org/10.1016/j.chemolab.2017.04.002 [CrossRef] [Google Scholar].

  20. de Juan A, Tauler R. Multivariate curve resolution (MCR) from 2000: progress in concepts and applications. Crit Rev Anal Chem. 2006; https://doi.org/10.1080/10408340600970005. CrossRef, Google Scholar.

  21. Assistência Técnica. Petrobras. http://sites.petrobras.com.br/minisite/assistenciatecnica/perguntas.asp. Accessed in 18 Jan 2019.

  22. ASTM D 7545-14. Standard test method for oxidation stability of middle distillate fuels – rapid small scale oxidation test (RSSOT). In: West Conshohocken (PA): ASTM International. 2014; https://www.astm.org/Standards/D7545.htm. Accessed 23 Oct 2018.

  23. ASTM D 86-12. Standard test method for distillation of petroleum products at atmospheric pressure. In: West Conshohocken (PA): ASTM International. 2013; https://www.astm.org/DATABASE.CART/HISTORICAL/D86-12.htm. Accessed 23 Oct 2018.

  24. ASTM D 7042-14. Standard test method for dynamic viscosity and density of liquids by Stabinger viscometer (and the calculation of kinematic viscosity). In: West Conshohocken (PA): ASTM International. 2014; https://www.astm.org/DATABASE.CART/HISTORICAL/D7042-14.htm. Accessed 23 Oct 2018.

  25. ASTM D 2500-11. Standard test method for cloud point of petroleum products. In: West Conshohocken (PA): ASTM International. 2011; https://www.astm.org/DATABASE.CART/HISTORICAL/D2500-11.htm. Accessed 23 Oct 2018.

  26. Kennard RW, Stone LA. Computer aided design of experiments. Technometrics. 1969; https://doi.org/10.2307/1266770 [CrossRef] [Google Scholar].

  27. Bro R, Smilde AK. Principal component analysis. Anal Methods. 2014; https://doi.org/10.1039/C3AY41907J [CrossRef] [Google Scholar].

  28. Eftekhari A, Forouzanfar M, Moghaddam HA, Alirezaie J. Block-wise 2D kernel PCA/LDA for face recognition. Inform Process Lett. 2010; https://doi.org/10.1016/j.ipl.2010.06.006 [CrossRef] [Google Scholar].

  29. Pontes MJC, Galvão RKH, Araújo MCU, Moreira PNT, Neto ODP, José GE, Saldanha TCB. The successive projections algorithm for spectral variable selection in classification problems. Chemom Intell Lab Syst. 2005; https://doi.org/10.1016/j.chemolab.2004.12.001 [CrossRef] [Google Scholar].

  30. Broadhursta D, Goodacrea R, Jones A, Rowland JJ, Kell DB. Genetic algorithms as a method for variable selection in multiple linear regression and partial least squares regression, with applications to pyrolysis mass spectrometry. Anal Chim Acta. 1997; https://doi.org/10.1016/S0003-2670(97)00065-2 [CrossRef] [Google Scholar].

  31. Dixon SJ, Brereton RG. Comparison of performance of five common classifiers represented as boundary methods: Euclidean distance to centroids, linear discriminant analysis, quadratic discriminant analysis, learning vector quantization and support vector machines, as dependent on data structure. Chemometr Intell Lab Syst. 2009; https://doi.org/10.1016/j.chemolab.2008.07.010 [CrossRef] [Google Scholar].

  32. Wu W, Mallet Y, Walczak B, Penninckx W, Massart DL, Heuerding S. Erni F. Comparison of regularized discriminant analysis, linear discriminant analysis and quadratic discriminant analysis. Applied to NIR data. Anal Chim Acta 1996; https://doi.org/10.1016/0003-2670(96)00142-0 [CrossRef] [Google Scholar].

  33. Geladi P, Kowalski BR. Partial least-squares regression: a tutorial. Anal. Chim. Acta. 1986; https://doi.org/10.1016/0003-2670(86)80028-9 [CrossRef] [Google Scholar].

  34. Smola AJ, Schölkopf B. A tutorial on support vector regression. Stat. Comput. 2004; https://doi.org/10.1023/B:STCO.0000035301.49549.88 [CrossRef] [Google Scholar].

  35. Alves JCL, Henriques CB, Poppi RJ. Determination of diesel quality parameters using support vector regression and near infrared spectroscopy for an in-line blending optimizer system. Fuel. 2012; https://doi.org/10.1016/j.fuel.2012.03.016 [CrossRef] [Google Scholar].

  36. Tauler R, Kowaslki B, Fleming S. Multivariate curve resolution applied to spectral data from multiple runs of an industrial process. Anal Chem. 1993; https://doi.org/10.1021/ac00063a019 [CrossRef] [Google Scholar].

  37. Jaumot J, Igne B, Anderso CA, Drennen JK, de Juan A. Blending process modeling and control by multivariate curve resolution. Talanta. 2013; https://doi.org/10.1016/j.talanta.2013.09.037 [CrossRef] [Google Scholar].

  38. Bro R, de Jong S. A fast non-negativity-constrained least squares algorithm. J Chemom. 1997; https://doi.org/10.1002/(SICI)1099-128X(199709/10)11:5<393::AID-CEM483>3.0.CO;2-L [CrossRef] [Google Scholar].

  39. Olivieri AC, Faber NM, Ferré J, Boqué R, Kalivas JH. Mark, H. Uncertainty estimation and figures of merit for multivariate calibration. Pure Appl Chem. 2006; https://doi.org/10.1351/pac200678030633 [CrossRef] [Google Scholar].

  40. Botella L, Bimbela F, Martin L, Arauzo J, Sanchez JL. Oxidation stability of biodiesel fuels and blends using the Rancimat and PetroOxy methods. Effect of 4-allyl-2,6-dimetoxiphenol and catechol as biodiesel additives on oxidation stability. Front Chem. 2014; https://doi.org/10.3389/fchem.2014.00043 [CrossRef] [Google Scholar].

  41. Karavalakis G, Stournas S, Karonis D. Evaluation of the oxidation stability of diesel/biodiesel blends. Fuel. 2010; https://doi.org/10.1016/j.fuel.2010.03.041 [CrossRef] [Google Scholar].

  42. Roy MM, Wang W, Alawi M. Performance and emissions of a diesel engine fueled by biodiesel-diesel, biodiesel-diesel-additive and kerosene-biodiesel blends. Energ Convers Manage. 2014; https://doi.org/10.1016/j.enconman.2014.04.033 [CrossRef] [Google Scholar].

  43. Yadav SR, Murthy KV, Mishra D, Baral B. Estimation of petrol and diesel adulteration with kerosene and assessment of usefulness of selected automobile fuel quality test parameters. IJEST. 2005; https://doi.org/10.1007/BF03325839 [CrossRef] [Google Scholar].

  44. Ziegler K, Manka J. The effect of mixing diesel fuels additized with kerosene and cloud point depressants. SAE Technical Paper 2000–01-2884. 2000; https://doi.org/10.4271/2000-01-2884 [CrossRef] [Google Scholar].

  45. Silverstein RM, Webster FX, Kiemle DJ. Spectrometric identification of organic compounds. 7th Ed. New Jersey: Jonh Wiley & Sons; 2005. [Google Scholar].

  46. Workman J Jr, Weyer JL. Practical guide to interpretive near-infrared spectroscopy. 1st ed. Boca Raton: CRC Press; 2008. [Google Scholar]

    Google Scholar 

  47. Yang C, Yang Z, Zhang G, Hollebone B, Landriault M, Wang Z, Lambert P, Brown CE. Characterization and differentiation of chemical fingerprints of virgin and used lubricating oils for identification of contamination or adulteration sources. Fuel. 2016; https://doi.org/10.1016/j.fuel.2015.09.070 [CrossRef] [Google Scholar].

  48. Divya O, Mishra AK. Multivariate methods on the excitation emission matrix fluorescence spectroscopic data of diesel-kerosene mixtures: a comparative study. Anal Chim Acta. 2007; https://doi.org/10.1016/j.aca.2007.03.079 [CrossRef] [Google Scholar].

  49. Monograph NIR spectroscopy. A guide to near-infrared spectroscopic analysis of industrial manufacturing processes. In: Metrohm NIR Systems. 2017. http://www.mep.net.au/wpmep/wpcontent/uploads/2013/05/MEP_Monograph_NIRS_81085026EN.pdf. Accessed 29 Oct 2018. [CrossRef] [Google Scholar].

Download references

Funding

This research received a financial support from the Post-Graduate Chemistry Program PPGQ/UFRN, the Energetic Technologies Laboratory (LTEN), the Biological Chemistry and Chemometrics Group, and the CAPES and CNPQ – Brazil.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Heloise O. M. A. Moura or Luciene S. de Carvalho.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(PDF 4.51 mb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moura, H.O.M.A., Câmara, A.B.F., Santos, M.C.D. et al. Advances in chemometric control of commercial diesel adulteration by kerosene using IR spectroscopy. Anal Bioanal Chem 411, 2301–2315 (2019). https://doi.org/10.1007/s00216-019-01671-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-019-01671-y

Keywords

Navigation