Anwar, H., Anwar, T., & Murtaza, S. (2023). Review on food quality assessment using machine learning and electronic nose system. Biosensors and Bioelectronics: X, 14, 100365.
Ardebili, S. M. S., Solmaz, H., İpci, D., Calam, A., & Mostafaei, M. (2020a). A review on higher alcohol of fusel oil as a renewable fuel for internal combustion engines: Applications, challenges, and global potential. Fuel, 279, 118516.
Ardebili, S. M. S., Taghipoor, A., Solmaz, H., & Mostafaei, M. (2020b). The effect of nano-biochar on the performance and emissions of a diesel engine fueled with fusel oil-diesel fuel. Fuel, 268, 117356.
Arshak, K., Moore, E., Lyons, G. M., Harris, J., & Clifford, S. (2004). A review of gas sensors employed in electronic nose applications. Sensor Review, 24, 181-198.
Balabin, R. M., & Lomakina, E. I. (2011). Support vector machine regression (SVR/LS-SVM) - An alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data. Analyst, 136(8), 1703–1712.
Beurey, C., Gozlan, B., Carré, M., Bacquart, T., Morris, A., Moore, N., Arrhenius, K., Meuzelaar, H., Persijn, S., Rojo, A., & Murugan, A. (2021). Review and survey of methods for analysis of impurities in hydrogen for fuel cell vehicles according to ISO 14687:2019. Frontiers in Energy Research, 8, 1–20.
Cabaneros Lopez, P., Feldman, H., Mauricio-Iglesias, M., Junicke, H., Huusom, J. K., & Gernaey, K. V. (2019). Benchmarking real-time monitoring strategies for ethanol production from lignocellulosic biomass. Biomass and Bioenergy, 127, 105296.
Calam, A., Içingür, Y., Solmaz, H., & Yamk, H. (2015). A comparison of engine performance and the emission of fusel oil and gasoline mixtures at different ignition timings. International Journal of Green Energy, 12(8), 767–772.
Cerrato Oliveros, M. C., Pérez Pavón, J. L., García Pinto, C., Fernández Laespada, M. E., Moreno Cordero, B., & Forina, M. (2002). Electronic nose based on metal oxide semiconductor sensors as a fast alternative for the detection of adulteration of virgin olive oils. Analytica Chimica Acta, 459(2), 219–228.
Cinar, C., Uyumaz, A., Solmaz, H., Sahin, F., Polat, S., & Yilmaz, E. (2015). Effects of intake air temperature on combustion, performance and emission characteristics of a HCCI engine fueled with the blends of 20% n-heptane and 80% isooctane fuels. Fuel Processing Technology, 130(C), 275–281.
Cunha, C. L., Luna, A. S., Oliveira, R. C. G., Xavier, G. M., Paredes, M. L. L., & Torres, A. R. (2017). Predicting the properties of biodiesel and its blends using mid-FT-IR spectroscopy and first-order multivariate calibration. Fuel, 204, 185–194.
Hassan Pour, A., Safieddin Ardebili, S. M., & Sheikhdavoodi, M. J. (2018). Multi-objective optimization of diesel engine performance and emissions fueled with diesel-biodiesel-fusel oil blends using response surface method. Environmental Science and Pollution Research, 25(35), 35429-35439.
Hu, G., Ahmed, M., & L’Abbé, M. R. (2023). Natural language processing and machine learning approaches for food categorization and nutrition quality prediction compared with traditional methods. American Journal of Clinical Nutrition, 117(3), 553–563.
Kelanic, R. A. (2016). The Petroleum Paradox: Oil, Coercive Vulnerability, and Great Power Behavior. Security Studies, 25(2), 181–213.
Khorramifar, A., Karami, H., Wilson, A. D., Sayyah, A. H. A., Shuba, A., & Lozano, J. (2022). Grape Cultivar Identification and Classification by Machine Olfaction Analysis of Leaf Volatiles. Chemosensors, 10(4), 1–17.
Lashgari, M., & Mohammadigol, R. (2016). Discrimination of Golab apple storage time using acoustic impulse response and LDA and QDA discriminant analysis techniques. Iran Agricultural Research, 35(2), 65–70.
Li, Y., Tang, W., Chen, Y., Liu, J., & Lee, C. fon F. (2019). Potential of acetone-butanol-ethanol (ABE) as a biofuel. Fuel, 242, 673–686.
Loutfi, A., Coradeschi, S., Mani, G. K., Shankar, P., & Rayappan, J. B. B. (2015). Electronic noses for food quality: A review. Journal of Food Engineering, 144, 103–111.
Mahmodi, K., Mostafaei, M., & Mirzaee-Ghaleh, E. (2019). Detection and classification of diesel-biodiesel blends by LDA, QDA and SVM approaches using an electronic nose. Fuel, 258, 116114.
Mahmodi, K., Mostafaei, M., & Mirzaee-Ghaleh, E. (2022). Detecting the different blends of diesel and biodiesel fuels using electronic nose machine coupled ANN and RSM methods. Sustainable Energy Technologies and Assessments, 51, 101914.
Panoutsou, C., Germer, S., Karka, P., Papadokostantakis, S., Kroyan, Y., Wojcieszyk, M., Maniatis, K., Marchand, P., & Landalv, I. (2021). Advanced biofuels to decarbonise European transport by 2030: Markets, challenges, and policies that impact their successful market uptake. Energy Strategy Reviews, 34, 100633.
Rasekh, M., Karami, H., Wilson, A. D., & Gancarz, M. (2021). Classification and identification of essential oils from herbs and fruits based on a mos electronic-nose technology. Chemosensors, 9(6), 1–16.
Ren, J., & Sovacool, B. K. (2015). Prioritizing low-carbon energy sources to enhance China’s energy security. Energy Conversion and Management, 92, 129–136.
Uyumaz, A. (2017). Emme Havası Giriş Sıcaklığı ve Ön Karışımlı Yakıt Oranının RCCI Yanma Karakteristiklerine ve Motor Performansına Etkileri. Journal of Polytechnic, 20, 689–698.
Wojnowski, W., Majchrzak, T., Dymerski, T., Gębicki, J., & Namieśnik, J. (2017). Portable electronic nose based on electrochemical sensors for food quality assessment. Sensors, 17(12), 1–14.
Xu, M., Li, X., Jia, P., & Zhang, L. (2023). Classification Techniques of Electronic Nose: A Review. International Journal of Bio-Inspired Computation, 1(1).