Determination of Adulteration of the B10 Blend of Diesel and Crambe Biodiesel Using Proton Nuclear Magnetic Resonance (¹H NMR) Spectroscopy with a Data Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) Model

References

• Alves, J. C. L., C. B. Henriques, and R. J. Poppi. 2014. Classification of diesel pool refinery streams through near infrared spectroscopy and support vector machines using C-SVC and ν-SVC. Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy 117 (January):389–96. https://linkinghub.elsevier.com/retrieve/pii/S1386142513008810. doi:10.1016/j.saa.2013.08.018.
   PubMedGoogle Scholar
• Brasil. 2014. Lei No. 13.033, de 24 Set. 2014: Dispõe Sobre a Adição Obrigatória de Biodiesel Ao Óleo Diesel Comercializado Com o Consumidor Final; Altera as Leis No 9.478, de 6 de Agosto de 1997, e 8.723, de 28 de Outubro de 1993; Revoga Dispositivos Da Lei No 11.097. https://www.cosif.com.br/captura.asp?arquivo=lo13033.
   Google Scholar
• Brasil. 2018. Análise de Conjuntura Dos Biocombustíveis Ano 2018. MME, Ministério de Minas e Energia. Rio de Janeiro: Copyright © 2017 EPE. http://www.epe.gov.br/Petroleo/Documents/An?lise de Conjuntura dos Biocombust?veis - boletins peri?dicos/An?lise de Conjuntura - Ano 2013.pdf.
   Google Scholar
• de Santana, F. B., W. Borges Neto, and R. J. Poppi. 2019. Random forest as one-class classifier and infrared spectroscopy for food adulteration detection. Food Chemistry 293:323–32. (September 30): https://www.sciencedirect.com/science/article/pii/S0308814619307368. doi:10.1016/j.foodchem.2019.04.073.
   PubMed Web of Science ®Google Scholar
• European Commission Directive 2012/0288 (COD)C7-0337/12. 2012. Proposal for a Directive of the European Parliament and of the Council Amending Directive 98/70/EC Relating to the Quality of Petrol and Diesel Fuels and Amending Directive 2009/28/EC on the Promotion of the Use of Energy from Renewable Sources. Brussels. http://www.europarl.europa.eu/meetdocs/2009_2014/documents/com/com_com(2012)0595_/com_com(2012)0595_en.pdf.
   Google Scholar
• Fan, S., Q. Zhong, C. Fauhl-Hassek, M. K. H. Pfister, B. Horn, and Z. Huang. 2018. Classification of Chinese wine varieties using 1H NMR spectroscopy combined with multivariate statistical analysis. Food Control 88 (June 1): 113–22. doi:10.1016/j.foodcont.2017.11.002.
   Web of Science ®Google Scholar
• Ferreira, M. M. C. 2015. Chemometrics: Concepts, Methods and Applications. Campinas-SP: Unicamp Publisher. http://books.scielo.org/id/2nc9y.
   Google Scholar
• Gupta, K., A. Bhavsar, and A. K. Sao. 2019. Detecting mitotic cells in HEp-2 images as anomalies via one class classifier. Computers in Biology and Medicine 111 (August 1):103328. https://www.sciencedirect.com/science/article/pii/S0010482519301970. doi:10.1016/j.compbiomed.2019.103328.
   PubMed Web of Science ®Google Scholar
• Janaun, J., and N. Ellis. 2010. Perspectives on biodiesel as a sustainable fuel. Renewable and Sustainable Energy Reviews 14 (4):1312–20. https://pdf.sciencedirectassets.com/271969/1-s2.0-S1364032110X0003X/1-s2.0-S1364032109002913/main.pdf?X-Amz-Security-Token=AgoJb3JpZ2luX2VjEOj%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJGMEQCIHZRxZUadn33xqtCTqd0IceFw9uLY4h%2Btd92wHLtmv8PAiAtVsqJe8Vbcm. doi:10.1016/j.rser.2009.12.011.
   Web of Science ®Google Scholar
• Jović, O., K. Pičuljan, T. Hrenar, T. Smolić, and I. Primožič. 2019. 1H NMR adulteration study of hempseed oil with full chemometric approach on large variable data. Chemometrics and Intelligent Laboratory Systems 185:41–6. (February 15): doi:10.1016/j.chemolab.2018.12.012.
   Web of Science ®Google Scholar
• Knothe, G. 2008. Designer” biodiesel: Optimizing fatty ester composition to improve fuel properties. Energy & Fuels 22 (2):1358–64. https://pubs.acs.org/sharingguidelines. doi:10.1021/ef700639e.
   Web of Science ®Google Scholar
• Knothe, G., and K. R. Steidley. 2005. Kinematic viscosity of biodiesel fuel components and related compounds. influence of compound structure and comparison to petrodiesel fuel components. Fuel 84 (9):1059–65. https://linkinghub.elsevier.com/retrieve/pii/S0016236105000475. doi:10.1016/j.fuel.2005.01.016.
   Web of Science ®Google Scholar
• Máquina, A. D. V., L. M. de Souza, L. C. Gontijo, D. Q. Santos, and W. Borges Neto. 2017. Characterization of biodiesel by infrared spectroscopy with partial least square discriminant analysis. Analytical Letters 50 (13):2117–28. doi:10.1080/00032719.2016.1267186.
   Web of Science ®Google Scholar
• Máquina, A. D. V., B. V. Sitoe, J. E. Buiatte, D. Q. Santos, and W. B. Neto. 2019. Quantification and classification of cotton biodiesel content in diesel blends, using mid-infrared spectroscopy and chemometric methods. Fuel 237:373–9. doi:10.1016/j.fuel.2018.10.011.
   Web of Science ®Google Scholar
• Mazivila, S. J. 2018. Trends of non-destructive analytical methods for identification of biodiesel feedstock in diesel-biodiesel blend according to European Commission Directive 2012/0288/EC and detecting diesel-biodiesel blend adulteration: A brief review. Talanta 180 (April):239–47. https://linkinghub.elsevier.com/retrieve/pii/S0039914017312596. doi:10.1016/j.talanta.2017.12.057.
   PubMed Web of Science ®Google Scholar
• Mazivila, S. J., L. C. Gontijo, F. B. De Santana, H. Mitsutake, D. Q. Santos, and W. Borges Neto. 2015. Fast detection of adulterants/contaminants in biodiesel/diesel blend (B5) employing mid-infrared spectroscopy and PLS-DA. Energy & Fuels 29 (1):227–32. doi:10.1021/ef502122w.
   Web of Science ®Google Scholar
• McMurry, J. 2016. Organic chemistry. Ed. Robson Mendes Matos. 9th ed. São Paulo: Cengage Learning.
   Google Scholar
• Monakhova, Y. B., and B. W. K. Diehl. 2015. Combining 1H NMR spectroscopy and multivariate regression techniques to quantitatively determine falsification of porcine heparin with bovine species. J Pharm Biomed Anal 115:543–51. (November 10): doi:10.1016/j.jpba.2015.08.017.
   PubMed Web of Science ®Google Scholar
• Monteiro, M. R., A. R. P. Ambrozin, L. M. Lião, and A. G. Ferreira. 2009. Determination of biodiesel blend levels in different diesel samples by 1H NMR. Fuel 88 (4):691–6. doi:10.1016/j.fuel.2008.10.010.
   Web of Science ®Google Scholar
• Pantazi, X. E., D. Moshou, and A. A. Tamouridou. 2019. Automated leaf disease detection in different crop species through image features analysis and one class classifiers. Computers and Electronics in Agriculture 156:96–104. https://www.sciencedirect.com/science/article/pii/S0168169918310202. doi:10.1016/j.compag.2018.11.005.
   Web of Science ®Google Scholar
• Pavia, D. L., G. M. Lampman, G. S. Kriz, and J. R. Vyvyan. 2010. Introduction to spectroscopy. Translation of the 4th North American Edition. 4th. São Paulo: CENGAGE Learning.
   Google Scholar
• Pires de Oliveira, I., and A. R. L. Caires. 2019. Molecular arrangement in diesel/biodiesel blends: A molecular dynamics simulation analysis. Renewable Energy. 140 (September):203–11. https://linkinghub.elsevier.com/retrieve/pii/S0960148119303660. doi:10.1016/j.renene.2019.03.061.
   Web of Science ®Google Scholar
• Pomerantsev, A. L. 2008. Acceptance areas for multivariate classification derived by projection methods. Journal of Chemometrics 22 (11-12):601–9. doi:10.1002/cem.1147.
   Web of Science ®Google Scholar
• Pomerantsev, A. L., and O. Y. Rodionova. 2014a. On the type II error in SIMCA method. Journal of Chemometrics 28 (6):518–22. doi:10.1002/cem.2610.
   Web of Science ®Google Scholar
• Pomerantsev, A. L., and O. Y. Rodionova. 2014b. Concept and role of extreme objects in PCA/SIMCA. Journal of Chemometrics 28 (5):429–38. doi:10.1002/cem.2506.
   Web of Science ®Google Scholar
• Portela, N. A., E. C. S. Oliveira, A. C. Neto, R. R. T. Rodrigues, S. R. C. Silva, E. V. R. Castro, and P. R. Filgueiras. 2016. Quantification of biodiesel in petroleum diesel by 1H NMR: Evaluation of univariate and multivariate approaches. Fuel 166:12–8. doi:10.1016/j.fuel.2015.10.091.
   Web of Science ®Google Scholar
• Rodionova, O. Y., P. Oliveri, and A. L. Pomerantsev. 2016. Rigorous and compliant approaches to one-class classification. Chemometrics and Intelligent Laboratory Systems 159:89–96. no. September: doi:10.1016/j.chemolab.2016.10.002.
   Web of Science ®Google Scholar
• Rodionova, O. Y., A. V. Titova, and A. L. Pomerantsev. 2016. Discriminant analysis is an inappropriate method of authentication. TrAC Trends in Analytical Chemistry 78 (April):17–22. https://linkinghub.elsevier.com/retrieve/pii/S0165993615302193. doi:10.1016/j.trac.2016.01.010.
   Google Scholar
• Savorani, F., G. Tomasi, and S. B. Engelsen. 2010. Icoshift: A versatile tool for the rapid alignment of 1D NMR spectra. Journal of Magnetic Resonance (San Diego, CA: 1997) 202 (2):190–202. February): https://linkinghub.elsevier.com/retrieve/pii/S1090780709003334. doi:10.1016/j.jmr.2009.11.012.
   PubMed Web of Science ®Google Scholar
• Shimamoto, G. G., and M. Tubino. 2016. Quantification of methanol in biodiesel through 1H nuclear magnetic resonance spectroscopy. Fuel 175:99–104. doi:10.1016/j.fuel.2016.02.019.
   Web of Science ®Google Scholar
• Sitoe, B. V., A. D. V. Máquina, F. B. De Santana, L. C. Gontijo, D. Q. Santos, and W. B. Neto. 2017. Monitoring of biodiesel content and adulterant presence in methyl and ethyl biodiesels of Jatropha in blends with mineral diesel using MIR spectrometry and multivariate control charts. Fuel 191:290–9. doi:10.1016/j.fuel.2016.11.078.
   Web of Science ®Google Scholar
• Sitoe, B. V., H. Mitsutake, E. Guimara, L. C. Gontijo, D. Q. Santos, and W. Borges Neto. 2016. Quality control of biodiesel content of B7 blends of methyl jatropha and methyl crambe biodiesels using mid-infrared spectroscopy and multivariate control charts based on net analyte signal. Energy & Fuels 30:1062–70. https://pubs.acs.org/sharingguidelines. doi:10.1021/acs.energyfuels.5b02489.
   Web of Science ®Google Scholar
• Vieira, A. P., N. A. Portela, Á. C. Neto, V. Lacerda, W. Romão, E. V. R. Castro, and P. R. Filgueiras. 2019. Determination of physicochemical properties of petroleum using 1H NMR spectroscopy combined with multivariate calibration. Fuel 253:320–6. doi:10.1016/j.fuel.2019.05.028.
   Web of Science ®Google Scholar
• Zontov, Y. V., O. Y. Rodionova, S. V. Kucheryavskiy, and A. L. Pomerantsev. 2017. DD-SIMCA – A MATLAB GUI Tool for data Driven SIMCA approach. Chemometrics and Intelligent Laboratory Systems 167:23–8. https://www.sciencedirect.com/science/article/pii/S0169743917301466#bib2. doi:10.1016/j.chemolab.2017.05.010.
   Web of Science ®Google Scholar