Rapid Assessment of Gasoline Quality by near-Infrared (NIR) Deep Learning Model Combined with Fractional Derivative Pretreatment

References

• Al Ibrahim, E., and A. Farooq. 2020. Octane prediction from infrared spectroscopic data. Energy & Fuels 34 (1):817–26. doi:https://doi.org/10.1021/acs.energyfuels.9b02816.
   Web of Science ®Google Scholar
• Alinezhad, K., M. Hosseini, K. Movagarnejad, and M. Salehi. 2010. Experimental and modeling approach to study separation of water in crude oil emulsion under non-uniform electrical field. Korean Journal of Chemical Engineering 27 (1):198–205. doi:https://doi.org/10.1007/s11814-009-0324-2.
   Web of Science ®Google Scholar
• Chen, H., Z. Liu, K. Cai, L. Xu, and A. Chen. 2018. Grid search parametric optimization for FT-NIR quantitative analysis of solid soluble content in strawberry samples. Vibrational Spectroscopy 94:7–15. doi:https://doi.org/10.1016/j.vibspec.2017.10.006.
   Web of Science ®Google Scholar
• Chen, H., X. Liu, A. Chen, K. Cai, and B. Lin. 2020. Parametric-scaling optimization of pretreatment methods for the determination of trace/quasi-trace elements based on near infrared spectroscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 229:117959. doi:https://doi.org/10.1016/j.saa.2019.117959.
   PubMed Web of Science ®Google Scholar
• Cozzolino, D. 2016. Near infrared spectroscopy as a tool to monitor contaminants in soil, sediments and water—State of the art, advantages and pitfalls. Trends in Environmental Analytical Chemistry 9:1–7. doi:https://doi.org/10.1016/j.teac.2015.10.001.
   Web of Science ®Google Scholar
• Cracknell, R. F., J. C. G. Andrae, L. J. McAllister, M. Norton, and H. L. Walmsley. 2009. The chemical origin of octane sensitivity in gasoline fuels containing nitroalkanes. Combustion and Flame 156 (5):1046–52. doi:https://doi.org/10.1016/j.combustflame.2008.12.001.
   Web of Science ®Google Scholar
• Cruz-Duarte, J. M., J. Rosales-Garcia, C. R. Correa-Cely, A. Garcia-Perez, and J. G. Avina-Cervantes. 2018. A closed form expression for the Gaussian–based Caputo–Fabrizio fractional derivative for signal processing applications. Communications in Nonlinear Science and Numerical Simulation 61:138–48.
   Web of Science ®Google Scholar
• Daly, S. R., K. E. Niemeyer, W. J. Cannella, and C. L. Hagen. 2016. Predicting fuel research octane number using Fourier-transform infrared absorption spectra of neat hydrocarbons. Fuel 183:359–65. doi:https://doi.org/10.1016/j.fuel.2016.06.097.
   Web of Science ®Google Scholar
• Delwiche, S. R., and J. B. Reeves. 2010. A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: Example with Savitzky-Golay filters and partial least squares regression. Applied Spectroscopy 64 (1):73–82. doi:https://doi.org/10.1366/000370210790572007.
   PubMed Web of Science ®Google Scholar
• Downes, G. M., C. E. Harwood, J. Wiedemann, N. Ebdon, H. Bond, and R. Meder. 2012. Radial variation in Kraft pulp yield and cellulose content in Eucalyptus globulus wood across three contrasting sites predicted by near infrared spectroscopy. Canadian Journal of Forest Research 42 (8):1577–86. doi:https://doi.org/10.1139/x2012-083.
   Web of Science ®Google Scholar
• Foong, T. M., K. J. Morganti, M. J. Brear, G. Da Silva, Y. Yang, and F. L. Dryer. 2014. The octane numbers of ethanol blended with gasoline and its surrogates. Fuel 115:727–39. doi:https://doi.org/10.1016/j.fuel.2013.07.105.
   Web of Science ®Google Scholar
• Gatius, F., C. Miralbés, C. David, and J. Puy. 2017. Comparison of CCA and PLS to explore and model NIR data. Chemometrics and Intelligent Laboratory Systems 164:76–82. doi:https://doi.org/10.1016/j.chemolab.2017.03.011.
   Web of Science ®Google Scholar
• Grassi, S., and C. Alamprese. 2018. Advances in NIR spectroscopy applied to process analytical technology in food industries. Current Opinion in Food Science 22:17–21. doi:https://doi.org/10.1016/j.cofs.2017.12.008.
   Web of Science ®Google Scholar
• Hong, Y., S. Chen, Y. Liu, Y. Zhang, L. Yu, Y. Chen, Y. Liu, H. Cheng, and Y. Liu. 2019. Combination of fractional order derivative and memory-based learning algorithm to improve the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy. Catena 174:104–16. doi:https://doi.org/10.1016/j.catena.2018.10.051.
   Web of Science ®Google Scholar
• Kulcsár, T., G. Sárossy, G. Bereznai, R. Auer, and J. Abonyi. 2013. Partial least squares model based process monitoring using near infrared spectroscopy. Chemical Engineering 57:15–20.
   Web of Science ®Google Scholar
• Li, B., and W. Xie. 2015. Adaptive fractional differential approach and its application to medical image enhancement. Computers & Electrical Engineering 45:324–35. doi:https://doi.org/10.1016/j.compeleceng.2015.02.013.
   Web of Science ®Google Scholar
• Liu, L., X. P. Ye, A. M. Saxton, and A. Womac. 2010. Pretreatment of near infrared spectral data in fast biomass analysis. Journal of near Infrared Spectroscopy 18 (5):317–31. doi:https://doi.org/10.1255/jnirs.898.
   Web of Science ®Google Scholar
• Morales-Delgado, V. F., J. F. Gómez-Aguilar, M. A. Taneco-Hernández, and R. F. Escobar-Jiménez. 2018. A novel fractional derivative with variable- and constant-order applied to a mass-spring-damper system. European Physical Journal Plus 133 (78):1–14.
   Google Scholar
• Moura-Bueno, J. M., R. S. D. Dalmolin, A. ten Caten, A. C. Dotto, and J. A. M. Demattê. 2019. Stratification of a local VIS-NIR-SWIR spectral library by homogeneity criteria yields more accurate soil organic carbon predictions. Geoderma 337:565–81. doi:https://doi.org/10.1016/j.geoderma.2018.10.015.
   Web of Science ®Google Scholar
• Nawar, S., H. Buddenbaum, and J. Hill. 2015. Estimation of soil salinity using three quantitative methods based on visible and near-infrared reflectance spectroscopy: A case study from Egypt. Arabian Journal of Geosciences 8 (7):5127–40. doi:https://doi.org/10.1007/s12517-014-1580-y.
   Web of Science ®Google Scholar
• Özdemir, D. 2005. Determination of octane number of gasoline using near infrared spectroscopy and genetic multivariate calibration methods. Petroleum Science and Technology 23 (9–10):1139–52. doi:https://doi.org/10.1081/LFT-200035547.
   Web of Science ®Google Scholar
• Pooseh, S., R. Almeida, and D. F. M. Torres. 2013. Numerical approximations of fractional derivatives with applications. Asian Journal of Control 15 (3):698–712. doi:https://doi.org/10.1002/asjc.617.
   Web of Science ®Google Scholar
• Roso, V. R., N. D. S. A. Santos, C. E. C. Alvarez, F. A. Rodrigues Filho, F. J. P. Pujatti, and R. M. Valle. 2019. Effects of mixture enleanment in combustion and emission parameters using a flex-fuel engine with ethanol and gasoline. Applied Thermal Engineering 153:463–72. doi:https://doi.org/10.1016/j.applthermaleng.2019.03.012.
   Web of Science ®Google Scholar
• Sampaio, P. S., A. Soares, A. Castanho, A. S. Almeida, J. Oliveira, and C. Brites. 2018. Optimization of rice amylose determination by NIR-spectroscopy using PLS chemometrics algorithms. Food Chemistry 242:196–204.
   PubMed Web of Science ®Google Scholar
• Savitzky, A., and M. J. E. Golay. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36 (8):1627–39. doi:https://doi.org/10.1021/ac60214a047.
   Web of Science ®Google Scholar
• Schirmer, W. N., L. Z. Olanyk, C. L. B. Guedes, T. P. Quessada, C. B. Ribeiro, and M. A. Capanema. 2017. Effects of air/fuel ratio on gas emissions in a small spark-ignited non-road engine operating with different gasoline/ethanol blends. Environmental Science and Pollution Research 24 (25):20354–9. doi:https://doi.org/10.1007/s11356-017-9651-8.
   PubMed Web of Science ®Google Scholar
• Srivastava, H. M. 2017. Remarks on some families of fractional-order differential equations. Integral Transforms and Special Functions 28 (7):560–4. doi:https://doi.org/10.1080/10652469.2017.1317248.
   Web of Science ®Google Scholar
• Teye, E., X. Huang, H. Dai, and Q. Chen. 2013. Rapid differentiation of Ghana cocoa beans by FT-NIR spectroscopy coupled with multivariate classification. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 114:183–9. doi:https://doi.org/10.1016/j.saa.2013.05.063.
   PubMed Web of Science ®Google Scholar
• Tian, D., D. Xue, and D. Wang. 2015. A fractional-order adaptive regularization primal-dual algorithm for image denoising. Information Sciences 296:147–59. doi:https://doi.org/10.1016/j.ins.2014.10.050.
   Web of Science ®Google Scholar
• Wan, M., M. Qu, W. Hu, W. Li, C. Zhang, H. Cheng, and B. Huang. 2019. Estimation of soil pH using PXRF spectrometry and Vis-NIR spectroscopy for rapid environmental risk assessment of soil heavy metals. Process Safety and Environmental Protection 132:73–81. doi:https://doi.org/10.1016/j.psep.2019.09.025.
   Web of Science ®Google Scholar
• Wang, S., S. Liu, J. Zhang, X. Che, Z. Wang, and D. Kong. 2020. Feasibility study on prediction of gasoline octane number using NIR spectroscopy combined with manifold learning and neural network. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 228:117836. doi:https://doi.org/10.1016/j.saa.2019.117836.
   PubMed Web of Science ®Google Scholar
• Wold, S., M. Sjöström, and L. Eriksson. 2001. PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58 (2):109–30. doi:https://doi.org/10.1016/S0169-7439(01)00155-1.
   Web of Science ®Google Scholar
• Yun, Y. H., H. D. Li, B. C. Deng, and D. S. Cao. 2019. An overview of variable selection methods in multivariate analysis of near-infrared spectra. TrAC Trends in Analytical Chemistry 113:102–15. doi:https://doi.org/10.1016/j.trac.2019.01.018.
   Web of Science ®Google Scholar
• Zhang, Z., J. Ding, J. Wang, and X. Ge. 2020. Prediction of soil organic matter in northwestern China using fractional-order derivative spectroscopy and modified normalized difference indices. Catena 185:104257. doi:https://doi.org/10.1016/j.catena.2019.104257.
   Web of Science ®Google Scholar