تشخیص فساد در رب گوجه فرنگی توسط باکتری باسیلوس سوبتیلیس، قارچ پنیسیلیوم و قارچ آسپرژیلوس به کمک بینی الکترونیکی

Astuti, S. D., Al Isyrofie, A. I. F., Nashichah, R., Kashif, M., Mujiwati, T., Susilo, Y., & Syahrom, A. (2022). Gas Array Sensors based on Electronic Nose for Detection of Tuna (Euthynnus Affinis) Contaminated by Pseudomonas Aeruginosa. Journal of Medical Signals & Sensors, 12(4), 306.

Alizadeh behbahani, B., Noshad, M., & Sahraiyan, B. (2021). Investigation of the minimum inhibitory concentration and the minimum bactericidal concentration of Eucalyptus globulus essential oil on a number of pathogenic bacteria and the cause of food spoilage. FSCT, 18 (110), 49-57. URL: http://fsct.modares.ac.ir/article-7-43058-fa.html.(In Persian).

Aghili Nategh, N., Anvar, A., & Dalvand, M. J. (2020). Detection of Ripeness Grades of Strawberry Using an Electronic Nose. Journal of Researches in Mechanics of Agricultural Machinery, 9(2), 71-80. magiran.com/p2199818

Adab, H., Farajzadeh, M., Filekash, A., & Esmaili, R. (2015). preparation of autumn rapeseed yield map using perceptron neural network, case study: Sabzevar city. Quarterly scientific research journal of geographical space, 13(41), 171-180.( In Persian)

Asogwa, O. C., & Oladugba, A. V. (2015). On the comparison of artificial neural network (ANN) and multinomial logistic regression (MLR). West African Journal of Industrial and Academic Research, 13(1), 3-9.

Al-Maskari, S., Li, X., & Liu Q. (2014). An effective approach to handling noise and drift in electronic noses. In: Wang H, Sharaf MA (eds) Databases theory and applications. Lecture Notes in Computer Science, Springer, Cham, pp, 223–230

Balasubramanian, S., Panigrahi, S., Logue, C., Marchello, M., Doetkott, C., Gu, H., Sherwood, J. & Nolan, L. (2009). Spoilage identification of beef using an electronic nosesystem. Transactions of the American Society of Agricultural Engineering, 47(5), 1625–1633.

Chinipardaz, R., Rekabdar, Q., & Yousefi Hajiabad, R. (2015). Studying the human development of countries using mixed audit analysis methods. Quarterly Journal of Economic Studies,3(3), 5-20.( In Persian).

Concina, I., Falasconi, M., Gobbi, E., Bianchi, F., Musci, M., Mattarozzi, M., Pardo, M., Mangia, A., Careri M., & Sberveglieri, G. (2009). Early detection of microbial contamination in processed tomatoes by electronic nose. Food Control, 20(10), 873-880.

Ghiami Rad, M., Akbari, R., & Safari, A. (2017). Effect of alcoholic extract of garlic (Allium sativum) on some food born pathogenic bacteria. Journal of Food Hygiene, 6(4), 53-102.(In Persian).

Guil-Guerrero, J. L., Ramos, L., Moreno, C., Zúñiga-Paredes, J. C., Carlosama-Yépez, M., & Ruales, P. (2016). Plant-food by-products to improve farm-animal health. Animal Feed Science and Technology, 220, 121-135.

Hassen, Y., Gebre, H., & Haile, A. (2019). Effects of Pre-Heating and concentration temperatures on Physico-Chemical quality of semi concentrated tomato (Solanum lycopersicum) paste. J. Food Process. Technol, 10(2). doi: 10.4172/2157-7110.1000795

Hawksworth, D. L. (2015). Naming fungi involved in spoilage of food, drink, and water. Current Opinion in Food Science, 5, 23-28.

Heidarbeigi, K., Mohtasebi, S. S., Foroughirad, A., Ghasemi-Varnamkhasti, M., Rafiee, S., & Rezaei, K. (2015). Detection of adulteration in saffron samples using electronic nose. International Journal of Food Properties, 18(7), 1391-1401.

Karazhyan, R., Habibi-najafi, M. H., Yavarmanesh, M., Edalatian dovom, M. R., & Pourianfar, H. (2017). Comparison of optimized DNA extraction from mold Aspergillus, Penicillium and Rhizopus of tomato paste. FSCT, 14(67), 1-9 .(In Persian).

Lianou, A., Panagou, E. Z., & Nychas, G. J. E. (2016). Microbiological spoilage of foods and beverages. Stab Shelf Life Food: 3–42.

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.

Modupalli, N., Naik, M., Sunil, C. K., & Natarajan, V. (2021). Emerging non-destructive methods for quality and safety monitoring of spices. Trends in Food Science & Technology, 108, 133-147.

Mu, F., Gu, Y., Zhang, J., & Zhang, L. (2020). Milk source identification and milk quality estimation using an electronic nose and machine learning techniques. Sensors, 20(15), 4238.

Mabood, F., Hussain, J., Jabeen, F., Abbas, G., Allaham, B. A., Albroumi, M., & Haq, Q. M. (2018). Applications of FT-NIRS combined with PLS multivariate methods for the detection & quantification of saccharin adulteration incommercial fruit juices. Food Addit Contam, 35(6), 1052-1060.

Møretrø, T., Moen, B., Heir, E., Hansen, A. A., & Langsrud, S. (2016). Contamination of salmon fillets and processing plants with spoilage bacteria. International Journal of Food Microbiology, 237, 98-108

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Pajohi, M. R., Tajik, H., & Farshid, A. A. (2010). Antimicrobial activity of Nisin on Bacillus cereus and Bacillus subtilis in a food model and their ultra structural investigation. Iranian Journal of Medical Microbiology, 4(3), 45-52.(In Persian).

Pearce, T. C., Schiffman, S. S., Nagle, H. T., & Gardner, J. W. (Eds.). (2006). Handbook of machine olfaction: electronic nose technology. John Wiley & Sons.

Roy, M., & Yadav, B. K. (2021). Electronic nose for detection of food adulteration: A review. Journal of Food Science and Technology, 1-13.

Rodriguez-Perez, R., Vogt, M., & Bajorath, J. (2017). Support vector machine classification and regression prioritize different structural features for binary compound activity and potency value prediction. ACS omega, 2(10), 6371-6379.

Rad, A. E., & Shahidi, F. (2004). Evaluation of physicochemical and microbial changes of bulk tomato paste in cold storage. Isfahan University of Technology-Journal of Crop Production and Processing, 8(1), 171-181.

Sanaeifar, A., Mohtasebi, S. S., Ghasemi-Varnamkhasti, M., & Ahmadi, H. (2016). Application of MOS based electronic nose for the prediction of banana quality properties. Measurement, 82, 105-114.

Triyana, K., Taukhid Subekti, M., Aji, P., Nur Hidayat, S., & Rohman, A. (2015). Development of electronic nose with low‑cost dynamic headspace for classifying vegetable oils and animal fats. In Applied Mechanics and Materials (Vol. 771, pp. 50‑54). Trans Tech Publications Ltd.

Vitalis, F., Zaukuu, J. L. Z., Bodor, Z., Aouadi, B., Hitka, G., Kaszab, T., Zsom-Muha, V., Gillay, Z., & Kovacs, Z. (2020). Detection and quantification of tomato paste adulteration using conventional and rapid analytical methods. Sensors, 20(21), 6059. doi:10.3390/s20216059.

Varidi, M. J., Varidi, M., Vajdi, M., & Sharifpour, A. (2018). Design, development and application of electronic nose instrument to rapidly detect spoilage of air, vacuum and modified atmosphere packaged camel minced meat. FSCT, 15 (74), 254-243. URL: http://fsct.modares.ac.ir/article-7-2048-fa.html. .(In Persian).

Zaki Dizaji, H., Adibzadeh, A., & Aghili Nategh, N. (2021). Application of E-nose technique to predict sugarcane syrup quality based on purity and refined sugar percentage. Journal of Food Science and Technology, 58, 4149-4156.