Discrimination of Biodiesel-Diesel of B7 and B10 by Infrared Spectroscopy with Dendogram

https://doi.org/10.22146/ijc.82499

Mohd Rashidi Abdull Manap(1*), Ahmad Fadly Jusoh(2), Lim Xiang Chuin(3), Nur Diana Farhana Muhamad Zulkifli(4), Qhurratul Aina Kholili(5), Fatin Abu Hasan(6), Danish Aiman Akmal Mohd Effendy(7), Ramizah Azis(8)

(1) Department of Chemistry Faculty of Science, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia
(2) Centre for Global Archaeological Research, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
(3) Department of Chemistry Faculty of Science, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia
(4) Department of Chemistry Faculty of Science, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia
(5) Department of Chemistry Faculty of Science, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia
(6) Department of Chemistry Faculty of Science, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia
(7) Department of Chemistry Faculty of Science, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia
(8) Jabatan Pengajian Umum IKTBN Sepang, Bandar Baru Salak Tinggi, Selangor 43900, Malaysia
(*) Corresponding Author

Abstract


Spectroscopists face an ongoing challenge in identifying fuel spectra due to a wide range of fuel formulations and the increasing abuse of biodiesel-diesel blends. In Malaysia, a new type of biodiesel-diesel blend known as B7 and B10 has been introduced, which requires rapid and reliable discrimination methods. However, current identification methods are costly and time-consuming. To overcome this issue, a spectroscopy study was conducted using a portable Fourier transform infrared (FTIR) spectrometer to identify biodiesel-diesel blends. The study found that direct identification using spectral libraries was reliable in identifying complex samples but unable to differentiate B7 and B10 due to the libraries' focus on hydrocarbons rather than esters. Instead, FTIR spectroscopy provided unique spectral peaks for each blend. Spectral range influences the discrimination, and the truncated region 1697–1777 and 1164–1224 cm−1 was shown to be reliable for discriminating the B7 and B10. The study concluded that a combination of algorithms, libraries, and hierarchical cluster analysis (HCA) in FTIR spectroscopy could effectively differentiate the blends. The primary objective was to differentiate B7 and B10 by analyzing liquid samples collected in Malaysia using HCA and IR spectroscopies. FTIR spectroscopy provides molecular-specific vibrational signals and is proven as a rapid identification method.


Keywords


biodiesel; diesel; discrimination; FTIR; Hierarchical Cluster Analysis (HCA)



References

[1] Naber, J.D., and Johnson, J.E., 2014, “Internal Combustion Engine Cycles and Concepts” in Alternative Fuels and Advanced Vehicle Technologies for Improved Environmental Performance, Eds. Folkson, R., Woodhead Publishing, Sawston, Cambridge, UK 197–224.

[2] Gad, S.C., 2014, “Petroleum Hydrocarbons” in Encyclopedia of Toxicology (Third Edition), Eds. Wexler, P., Academic Press, Oxford, UK, 838–840.

[3] da Silva, N.C., Pimentel, M.F., Honorato, R.S., Talhavini, M., Maldaner, A.O., and Honorato, F.A., 2015, Classification of Brazilian and foreign gasolines adulterated with alcohol using infrared spectroscopy, Forensic Sci. Int., 253, 33–42.

[4] Chowdhury, M., Gholizadeh, A., and Agah, M., 2021, Rapid detection of fuel adulteration using microfabricated gas chromatography, Fuel, 286, 119387.

[5] Bassbasi, M., Hafid, A., Platikanov, S., Tauler, R., and Oussama, A., 2013, Study of motor oil adulteration by infrared spectroscopy and chemometrics methods, Fuel, 104, 798–804.

[6] Nims, M.K., Melville, A.M., Moran, J.J., Jarman, K.H., and Wright, B.W., 2022, Compound specific stable isotope analysis of aromatics in diesel fuel to identify potential cocktailing, Forensic Sci. Int., 334, 111244.

[7] Azevedo, R.N.A., Bezerra, K.M.M., Nascimento, R.F., Nelson, R.K., Reddy, C.M., Nascimento, A.P., Oliveira, A.H.B., Martins, L.L., and Cavalcante, R.M., 2022, Is there a similarity between the 2019 and 2022 oil spills that occurred on the coast of Ceará (Northeast Brazil)? An analysis based on forensic environmental geochemistry, Environ. Pollut., 314, 120283.

[8] Cui, C., Zhang, L., Ma, Y., Billa, T., Hou, Z., Shi, Q., Zhao, S., Xu, C., and Klein, M.T., 2018, Computer-aided gasoline compositional model development based on GC-FID analysis, Energy Fuels, 32 (8), 8366–8373.

[9] Suppajariyawat, P., de Andrade, A.F.B., Elie, M., Baron, M., and Gonzalez-Rodriguez, J., 2019, The use of chemical composition and additives to classify petrol and diesel using gas chromatography-mass spectrometry and chemometric analysis: A UK study, Open Chem., 17 (1), 183–197.

[10] Rodriguez, J.D., Westenberger, B.J., Buhse, L.F., and Kauffman, J.F., 2011, Standardization of Raman spectra for transfer of spectral libraries across different instruments, Analyst, 136 (20), 4232–4240.

[11] Fremout, W., and Saverwyns, S., 2012, Identification of synthetic organic pigments: the role of a comprehensive digital Raman spectral library, J. Raman Spectrosc., 43 (11), 1536–1544.

[12] Ghosal, S., and Fang, H., 2015, Raman spectroscopy based identification of flame retardants in consumer products using an acquired reference spectral library, Talanta, 132, 635–640.

[13] Kaiser, C.R., Borges, J.L., dos Santos, A.R., Azevedo, D.A., and D’Avila, L.A., 2010, Quality control of gasoline by 1H NMR: Aromatics, olefinics, paraffinics, and oxygenated and benzene contents, Fuel, 89 (1), 99–104.

[14] Ferdous, A.H.M.I., Anower, M.S., Musha, A., Habib, M.A., and Shobug, M.A., 2022, A heptagonal PCF-based oil sensor to detect fuel adulteration using terahertz spectrum, Sens. Bio-Sens. Res., 36, 100485.

[15] Almeida, M.R., Logrado, L.P.L., Zacca, J.J., Correa, D.N., and Poppi, R.J., 2017, Raman hyperspectral imaging in conjunction with independent component analysis as a forensic tool for explosive analysis: The case of an ATM explosion, Talanta, 174, 628–632.

[16] Choi, S., and Yoh, J.J., 2017, Fire debris analysis for forensic fire investigation using laser induced breakdown spectroscopy, Spectrochim. Acta, Part B, 134, 75–80.

[17] Jamwal, R., Amit, A., Kumari, S., Sharma, S., Kelly, S., Cannavan, A., and Singh, D.K., 2021, Recent trends in the use of FTIR spectroscopy integrated with chemometrics for the detection of edible oil adulteration, Vib. Spectrosc., 113, 103222.

[18] Kepenek, E.S., Severcan, M., Gozen, A.G., and Severcan, F., 2020, Discrimination of heavy metal acclimated environmental strains by chemometric analysis of FTIR spectra, Ecotoxicol. Environ. Saf., 202, 110953.

[19] Menges, F., 2022, Spectragryph - Optical Spectroscopy Software, Version 1.2.16.1, http://www.effemm2.de/spectragryph/.

[20] Bekker, M., Louw, N.R., Jansen Van Rensburg, V.J., and Potgieter, J., 2013, The benefits of Fischer-Tropsch waxes in synthetic petroleum jelly, Int. J. Cosmet. Sci., 35 (1), 99–104.

[21] Zimmer, A., Cazarolli, J., Teixeira, R.M., Viscardi, S.L.C., Cavalcanti, E.S.H., Gerbase, A.E., Ferrão, M.F., Piatnicki, C.M.S., and Bento, F.M., 2013, Monitoring of efficacy of antimicrobial products during 60 days storage simulation of diesel (B0), biodiesel (B100) and blends (B7 and B10), Fuel, 112, 153–162.

[22] Zhang, X., Zhang, L., Li, J., Zou, X., Jing, X., and Li, W., 2022, Combustion and emission characteristics of diesel with different distillation ranges on the China-VI diesel engine, Fuel, 325, 124876.

[23] Barra, I., Kharbach, M., Qannari, E.M., Hanafi, M., Cherrah, Y., and Bouklouze, A., 2020, Predicting cetane number in diesel fuels using FTIR spectroscopy and PLS regression, Vib. Spectrosc., 111, 103157.

[24] Bukkarapu, K.R., and Krishnasamy, A., 2022, Predicting engine fuel properties of biodiesel and biodiesel-diesel blends using spectroscopy based approach, Fuel Process. Technol., 230, 107227.

[25] Chen, C., Liang, R., Xia, S., Hou, D., Abdoulaye, B., Tao, J., Yan, B., Cheng, Z., and Chen, G., 2023, Fast characterization of biodiesel via a combination of ATR-FTIR and machine learning models, Fuel, 332, 126177.



DOI: https://doi.org/10.22146/ijc.82499

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