Aghili, N.S., Rasekh, M., Karami, H., Azizi, V., & Gancarz, M. (2022). Detection of fraud in sesame oil with the help of artificial intelligence combined with chemometrics methods and chemical compounds characterization by gas chromatography–mass spectrometry. LWT- Food Sicence and Thecnology, 167, 113863.
Ayari, F., Mirzaee- Ghaleh, E., Rabbani, H., & Heidarbeigi, K. (2018). Using an E-nose machine for detection the adulteration of margarine in cow ghee. Journal of Food Process Engineering, 41(6), e12806.
Cerrato Oliveros, M. C., Pérez Pavón, J. L., Garcı́a 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.
Gómez, A. H., Wang, J., Hu, G., & Pereira, A. G. (2006). Electronic nose technique potential monitoring mandarin maturity. Sensors and Actuators B: Chemical, 113(1): 347-353.
Gupta, S., Rahman, M. A., & Sundaram, S. (2021). Citrus fruit as A potential source of phytochemical, antioxidant and pharmacological ingredients. Journal of Science and Healthcare Exploration (JSHE) ISSN, 2581, 8473.
Hong, X., Wang, J., & Qi, G. (2015). E-nose combined with chemometrics to trace tomato-juice quality. Journal of Food Engineering, 149: 38-43.
Karami, H., Rasekh, M., & Mirzaee – Ghaleh, E. (2020a). Comparison of chemometrics and AOCS official methods for predicting the shelf life of edible oil. Chemometrics and Intelligent Laboratory Systems, 206, 104165.
Karami, H., Rasekh, M., & Mirzaee-Ghaleh, E. (2020b). Application of the E-nose machine system to detect adulterations in mixed edible oils using chemometrics methods. Journal of Food Processing and Preservation, 44(9), e14696.
Karami, H., Rasekh, M., & Mirzaee-Ghaleh, E. (2020c). Qualitative analysis of edible oil oxidation using an olfactory machine. Journal of Food Measurement and Characterization, 14(5): 2600-2610.
Karami, H., Rasekh, M., & Mirzaee-Ghaleh, E. (2021). Identification of olfactory characteristics of edible oil during storage period using metal oxide semiconductor sensor signals and ANN methods. Journal of Food Processing and Preservation, 45(10), e15749.
Khodamoradi, F., Mirzaee-Ghaleh, E., Dalvand, M. J., & Sharifi, R. (2021). Classification of basil plant based on the level of consumed nitrogen fertilizer using an olfactory machine. Food Analytical Methods, 14: 2617-2629.
Khorramifar, A., Karami, H., Wilson, A.D., Sayyah, A.H.A., Shuba, A., & Lozano, J. (2022b). Grape Cultivar Identification and Classification by Machine Olfaction Analysis of Leaf Volatiles. Chemosensors, 10, 125,
Khorramifar, A., Rasekh, M., Karami, H., Covington, J.A., Derakhshani, S.M., Ramos, J., & Gancarz, M. (2022a). Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes. Molecules, 27, 3508,
Khorramifar, A., Rasekh, M., Karami, H., Malaga-Toboła, U., & Gancarz, M. (2021). A Machine Learning Method for Classification and Identification of Potato Cultivars Based on the Reaction of MOS Type Sensor-Array. Sensors, 21(17): 5836.
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. ( In Persian)
Luo, Y., Li, Z., Yuan, Y., & Yue, T. (2016). Bioadsorption of patulin from kiwi fruit juice onto a superior magnetic chitosan. Journal of Alloys and Compounds, 667: 101-108.
Lyu, W., Yuan, B., Liu, S., Simon, J. E., & Wu, Q. (2022). Assessment of lemon juice adulteration by targeted screening using LC-UV-MS and untargeted screening using UHPLC-QTOF/MS with machine learning. Food Chemistry, 373, 131424.
Mantha, M., Kubachka, K. M., Urban, J. R., Dasenbrock, C. O., Chernyshev, A., Mark, W. A., & Qi, H. (2019). Economically Motivated Adulteration of Lemon Juice: Cavity Ring Down Spectroscopy in Comparison with Isotope Ratio Mass Spectrometry: Round-Robin Study. Journal of AOAC Intternational, 102(5): 1544-1551.
Mohammadian, A., Barzegar, M., & Mani-Varnosfaderani, A. (2021). Detection of fraud in lime juice using pattern recognition techniques and FT-IR spectroscopy. Food Science & Nutrition, 9(6): 3026-3038.
Qiu, S., Wang, J., & Du, D. (2017). Assessment of high pressure processed mandarin juice in the headspace by using electronic nose and chemometric analysis. Innovative Food Science & Emerging Technologies, 42: 33-41.
Qiu, S., Wang, J., & Gao, L. (2015). Qualification and quantization of processed strawberry juice based on electronic nose and tongue. Lwt - Food Science and Technology, 60(1): 115-123.
Rasekh, M., & Karami, H. (2021a). Application of electronic nose with chemometrics methods to the detection of juices fraud. Journal of Food Processing and Preservation, 45(5), e15432.
Rasekh, M., & Karami, H. (2021b). E-nose coupled with an artificial neural network to detection of fraud in pure and industrial fruit juices. International Journal of Food Properties, 24(1): 592-602.
Rasekh, M., Karami, H., Fuentes, S., Kaveh, M., Rusinek, R., & Gancarz, M. (2022). Preliminary study non-destructive sorting techniques for pepper (Capsicum annuum L.) using odor parameter. LWT- Food Science and Thecnology, 164: 113667.
Rasekh, M., Karami, H., Wilson, A. D., & Gancarz, M. (2021a). Classification and Identification of Essential Oils from Herbs and Fruits Based on a MOS Electronic-Nose Technology. Chemosensors, 9(6): 142.
Rasekh, M., Karami, H., Wilson, A. D., & Gancarz, M. (2021b). Performance Analysis