Facile Detection of Oil Adulteration using UV-Visible Spectroscopy Coupled with Chemometrics Analysis
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Keywords:
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Lubricant oil, adulteration, UV-Visible spectroscopy, chemometrics, multivariate analysis.
Abstract
Engine and machine oils, better known as lubricant, is a fast-moving part within the motorcycle and automobile industries. Due to its high demand, these oils are often counterfeited by irresponsible people to get more profit. The thing most often done to adulterate oil is by mixing it with other low-quality or used oil. Here, we propose a simple analytical method to identify oil adulteration by using UV-Visible spectroscopy. A number of 425 genuine and adulterated oils were used as samples. After appropriate dilution using n-hexane, the samples were analyzed by UV-Visible spectrophotometer followed by Principle Component Analysis (PCA) and Principle Component Regression (PCR) as part of the chemometrics analysis. The results show that prediction samples were accurately classified into their corresponding groups with PCA scores of 49% and 27% for principal component 1 and 2, respectively. PLS model achieved a good prediction to detect lubricant oil adulteration, with R-Square of predicted and reference samples were 0.9257 and 0.9204, respectively. The proposed method shows a promising alternative to the conventional chemical method using a more sophisticated instruments such as GC-MS and HPLC for oil or other organic compound identification.
References
Bhaskar, R., Bhaskar, R., Sagar, M. K., Saini, V., & Bhat, K. (2012). Simultaneous Determination of Verapamil Hydrochloride and Gliclazide in Synthetic Binary Mixture and Combined Tablet Preparation by Chemometric-Assisted Spectroscopy. Journal of Analytical Sciences, Methods and Instrumentation, 2, 161–166.
Chen, Y., Zhu, S., Xie, M., Nie, S., Liu, W., Li, C., … Wang, Y. (2008). Quality control and original discrimination of Ganoderma lucidum based on high-performance liquid chromatographic fingerprints and combined chemometrics methods. Analytica Chimica Acta, 623, 146–156. https://doi.org/10.1016/j.aca.2008.06.018
de Carvalho Polari Souto, U. T., Barbosa, M. F., Dantas, H. V., de Pontes, A. S., Lyra, W. da S., Diniz, P. H. G. D., … da Silva, E. C. (2015). Identification of adulteration in ground roasted coffees using UV-Vis spectroscopy and SPA-LDA. LWT - Food Science and Technology, 63, 1037–1041. https://doi.org/10.1016/j.lwt.2015.04.003
Dinc, E., & Ustundag, O. (2003). Spectophotometric quantitative resolution of hydrochlorothiazide and spironolactone in tablets by chemometric analysis methods. Il Farmaco, 58, 1151–1161. https://doi.org/10.1016/j.farmac.2003.07.005
Escandar, G. M., Damiani, P. C., Goicoechea, C., & Olivieri, A. C. (2006). A review of multivariate calibration methods applied to biomedical analysis. Microchemical Journal, 82, 29–42. https://doi.org/10.1016/j.microc.2005.07.001
Firdaus, M. L., Aprian, A., Meileza, N., Hitsmi, M., Elvia, R., Rahmidar, L., & Khaydarov, R. (2019). Smartphone Coupled with a Paper-Based Colorimetric Device for Sensitive and Portable Mercury Ion Sensing. Chemosensors, 7, 25.
Foca, G., Masino, F., Antonelli, A., & Ulrici, A. (2011). Prediction of compositional and sensory characteristics using RGB digital images and multivariate calibration techniques. Analytica Chimica Acta, 706, 238–245. https://doi.org/10.1016/j.aca.2011.08.046
Gröger, T., & Zimmermann, R. (2011). Application of parallel computing to speed up chemometrics for GC × GC – TOFMS based metabolic fingerprinting. Talanta, 83, 1289–1294. https://doi.org/10.1016/j.talanta.2010.09.015
Hadad, G. M., El-Gindy, A., & Mahmoud, W. M. M. (2008). HPLC and chemometrics-assisted UV-spectroscopy methods for the simultaneous determination of ambroxol and doxycycline in capsule. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, 70(3), 655–663. https://doi.org/10.1016/j.saa.2007.08.016
Hanrahan, G. (2008). Environmental Chemometrics: Principles and Modern Applications. CRC Press.
Herrero-latorre, C., Barciela-garcía, J., García-martín, S., & Peña-crecente, R. M. (2019). Detection and quantification of adulterations in aged wine using RGB digital images combined with multivariate chemometric techniques. Food Chemistry: X, 3, 100046. https://doi.org/10.1016/j.fochx.2019.100046
Iłowska, J., Chrobak, J., Id, R. G., Szmatoła, M., Woch, J., Szwach, I., … Wrona, M. (2018). Designing Lubricating Properties of Vegetable Base Oils. Molecules, 23, 2025. https://doi.org/10.3390/molecules23082025
Liu, S., Saha, B., & Vlachos, D. G. (2019). Catalytic Production of Renewable Lubricant Base-Oils from Bio-Based 2-Alkylfurans and Enals. Green Chemistry, 21, 3606–3614.
Liu, S., Josephson, T. R., Athaley, A., Chen, Q. P., Norton, A., Ierapetritou, M., … Vlachos, D. G. (2019). Renewable lubricants with tailored molecular architecture. Science Advances, 5, 5487.
Martelo-Vidal, M. J., & Vázquez, M. (2014). Determination of polyphenolic compounds of red wines by UV-VIS-NIR spectroscopy and chemometrics tools. Food Chemistry, 158, 28–34. https://doi.org/10.1016/j.foodchem.2014.02.080
Miller, J. N., & Miller, J. C. (2010). Statistics and Chemometrics for Analytical Chemistry. Pearson/Prentice Hall.
Mobaraki, N., & Hemmateenejad, B. (2011). Chemometrics and Intelligent Laboratory Systems Structural characterization of carbonyl compounds by IR spectroscopy and chemometrics data analysis. Chemometrics and Intelligent Laboratory Systems, 109(2), 171–177. https://doi.org/10.1016/j.chemolab.2011.08.011
Nunes, C. A. (2014). Vibrational spectroscopy and chemometrics to assess authenticity, adulteration and intrinsic quality parameters of edible oils and fats. Food Research International, 60, 255–261. https://doi.org/10.1016/j.foodres.2013.08.041
Pan, R., Guo, F., Lu, H., & Feng, W. (2011). Journal of Pharmaceutical and Biomedical Analysis Development of the chromatographic fingerprint of Scutellaria barbata D . Don by GC – MS combined with Chemometrics methods. Journal of Pharmaceutical and Biomedical Analysis, 55(3), 391–396. https://doi.org/10.1016/j.jpba.2011.01.016
Sirisomboon, P., & Posom, J. (2019). On-line measurement of activation energy of ground bamboo using near infrared spectroscopy. Renewable Energy, 133, 480–488. https://doi.org/10.1016/j.renene.2018.10.051
Tan, J., Li, R., & Jiang, Z.-T. (2015). Chemometric classification of Chinese lager beers according to manufacturer based on data fusion of fluorescence, UV and visible spectroscopies. Food Chemistry, 184, 30–36. https://doi.org/10.1016/j.foodchem.2015.03.085
Tovar, J. S., Valbuena-duarte, S., & Racedo-niebles, F. (2018). Study of non-linear optical properties in automobile lubricating oil via Z-Scan technique. Revista Facultad de Ingeniería, (86), 27–31. https://doi.org/10.17533/udea.redin.n86a04
Wiberg, K. (2006). Quantitative impurity profiling by principal component analysis of high-performance liquid chromatography-diode array detection data. Journal of Chromatography A, 1108, 50–67. https://doi.org/10.1016/j.chroma.2005.12.077
Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58, 109–130. https://doi.org/10.1016/S0169-7439(01)00155-1
Chen, Y., Zhu, S., Xie, M., Nie, S., Liu, W., Li, C., … Wang, Y. (2008). Quality control and original discrimination of Ganoderma lucidum based on high-performance liquid chromatographic fingerprints and combined chemometrics methods. Analytica Chimica Acta, 623, 146–156. https://doi.org/10.1016/j.aca.2008.06.018
de Carvalho Polari Souto, U. T., Barbosa, M. F., Dantas, H. V., de Pontes, A. S., Lyra, W. da S., Diniz, P. H. G. D., … da Silva, E. C. (2015). Identification of adulteration in ground roasted coffees using UV-Vis spectroscopy and SPA-LDA. LWT - Food Science and Technology, 63, 1037–1041. https://doi.org/10.1016/j.lwt.2015.04.003
Dinc, E., & Ustundag, O. (2003). Spectophotometric quantitative resolution of hydrochlorothiazide and spironolactone in tablets by chemometric analysis methods. Il Farmaco, 58, 1151–1161. https://doi.org/10.1016/j.farmac.2003.07.005
Escandar, G. M., Damiani, P. C., Goicoechea, C., & Olivieri, A. C. (2006). A review of multivariate calibration methods applied to biomedical analysis. Microchemical Journal, 82, 29–42. https://doi.org/10.1016/j.microc.2005.07.001
Firdaus, M. L., Aprian, A., Meileza, N., Hitsmi, M., Elvia, R., Rahmidar, L., & Khaydarov, R. (2019). Smartphone Coupled with a Paper-Based Colorimetric Device for Sensitive and Portable Mercury Ion Sensing. Chemosensors, 7, 25.
Foca, G., Masino, F., Antonelli, A., & Ulrici, A. (2011). Prediction of compositional and sensory characteristics using RGB digital images and multivariate calibration techniques. Analytica Chimica Acta, 706, 238–245. https://doi.org/10.1016/j.aca.2011.08.046
Gröger, T., & Zimmermann, R. (2011). Application of parallel computing to speed up chemometrics for GC × GC – TOFMS based metabolic fingerprinting. Talanta, 83, 1289–1294. https://doi.org/10.1016/j.talanta.2010.09.015
Hadad, G. M., El-Gindy, A., & Mahmoud, W. M. M. (2008). HPLC and chemometrics-assisted UV-spectroscopy methods for the simultaneous determination of ambroxol and doxycycline in capsule. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, 70(3), 655–663. https://doi.org/10.1016/j.saa.2007.08.016
Hanrahan, G. (2008). Environmental Chemometrics: Principles and Modern Applications. CRC Press.
Herrero-latorre, C., Barciela-garcía, J., García-martín, S., & Peña-crecente, R. M. (2019). Detection and quantification of adulterations in aged wine using RGB digital images combined with multivariate chemometric techniques. Food Chemistry: X, 3, 100046. https://doi.org/10.1016/j.fochx.2019.100046
Iłowska, J., Chrobak, J., Id, R. G., Szmatoła, M., Woch, J., Szwach, I., … Wrona, M. (2018). Designing Lubricating Properties of Vegetable Base Oils. Molecules, 23, 2025. https://doi.org/10.3390/molecules23082025
Liu, S., Saha, B., & Vlachos, D. G. (2019). Catalytic Production of Renewable Lubricant Base-Oils from Bio-Based 2-Alkylfurans and Enals. Green Chemistry, 21, 3606–3614.
Liu, S., Josephson, T. R., Athaley, A., Chen, Q. P., Norton, A., Ierapetritou, M., … Vlachos, D. G. (2019). Renewable lubricants with tailored molecular architecture. Science Advances, 5, 5487.
Martelo-Vidal, M. J., & Vázquez, M. (2014). Determination of polyphenolic compounds of red wines by UV-VIS-NIR spectroscopy and chemometrics tools. Food Chemistry, 158, 28–34. https://doi.org/10.1016/j.foodchem.2014.02.080
Miller, J. N., & Miller, J. C. (2010). Statistics and Chemometrics for Analytical Chemistry. Pearson/Prentice Hall.
Mobaraki, N., & Hemmateenejad, B. (2011). Chemometrics and Intelligent Laboratory Systems Structural characterization of carbonyl compounds by IR spectroscopy and chemometrics data analysis. Chemometrics and Intelligent Laboratory Systems, 109(2), 171–177. https://doi.org/10.1016/j.chemolab.2011.08.011
Nunes, C. A. (2014). Vibrational spectroscopy and chemometrics to assess authenticity, adulteration and intrinsic quality parameters of edible oils and fats. Food Research International, 60, 255–261. https://doi.org/10.1016/j.foodres.2013.08.041
Pan, R., Guo, F., Lu, H., & Feng, W. (2011). Journal of Pharmaceutical and Biomedical Analysis Development of the chromatographic fingerprint of Scutellaria barbata D . Don by GC – MS combined with Chemometrics methods. Journal of Pharmaceutical and Biomedical Analysis, 55(3), 391–396. https://doi.org/10.1016/j.jpba.2011.01.016
Sirisomboon, P., & Posom, J. (2019). On-line measurement of activation energy of ground bamboo using near infrared spectroscopy. Renewable Energy, 133, 480–488. https://doi.org/10.1016/j.renene.2018.10.051
Tan, J., Li, R., & Jiang, Z.-T. (2015). Chemometric classification of Chinese lager beers according to manufacturer based on data fusion of fluorescence, UV and visible spectroscopies. Food Chemistry, 184, 30–36. https://doi.org/10.1016/j.foodchem.2015.03.085
Tovar, J. S., Valbuena-duarte, S., & Racedo-niebles, F. (2018). Study of non-linear optical properties in automobile lubricating oil via Z-Scan technique. Revista Facultad de Ingeniería, (86), 27–31. https://doi.org/10.17533/udea.redin.n86a04
Wiberg, K. (2006). Quantitative impurity profiling by principal component analysis of high-performance liquid chromatography-diode array detection data. Journal of Chromatography A, 1108, 50–67. https://doi.org/10.1016/j.chroma.2005.12.077
Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58, 109–130. https://doi.org/10.1016/S0169-7439(01)00155-1
Authors
Gusti, N., Oktarina, D., Elvia, R., Nursa’adah, E., Wardhana, R. W., Sudaryono, A., & Firdaus, M. L. (2021). Facile Detection of Oil Adulteration using UV-Visible Spectroscopy Coupled with Chemometrics Analysis. Science and Technology Indonesia, 6(1), 14–18. https://doi.org/10.26554/sti.2021.6.1.14-18
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