[1] Ellis, D. I., & Goodacre, R. (2001). Rapid and quantitative detection of the microbial spoilage of muscle foods: Current status and future trends. Trends in Food Science &Technology, 12, 414−424.
[2] Kamruzzaman, M., ElMasry, G., Sun, D.-W., Allen, P. (2012). Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis, Anal. Chim. Acta 714 57–67.
[3] Cai, J., Chen, Q., Wan, X., Zhao, J. (2011). Determination of total volatile basic nitrogen (TVB-N) content and Warner-Bratzler shear force (WBSF) in pork using Fourier transform near infrared (FT-NIR) spectroscopy. Food Chem. 126, 1354–1360.
[4] Tao, F., Peng, Y., Li, Y., Chao, K., Dhakal, S. (2012). Simultaneous determination oftenderness and Escherichia coli contamination of pork using hyperspectral scattering technique.Meat Sci. 90, 851–857.
[5] Xiong, Z., Sun, D.-W., Pu, H., Xie, A., Han, Z., Luo, M. (2015). Non-destructive prediction of thiobarbituric acid reactive substances (TBARS) value for freshness evaluation of chicken meat using hyperspectral imaging. Food Chemistry, 179, 175–181.
[6]Ramirez, R., Cava, R. (2007). The crossbreeding of different Duroc lines with the Iberian pig affects colour and oxidative stability of meat during storage. Meat Science, 77, 339–347.
[7] Li, H., Kutsanedzie, F., Zhao, J., & Chen, Q., (2016). Quantifying Total Viable Count in Pork Meat Using Combined Hyperspectral Imaging and Artificial Olfaction Techniques. Food Analytical Methods, 9(11), 3015–3024.
[8] Chmiel, M., Słowinski, M. (2016). The use of computer vision system to detect pork defects. LWT - Food Science and Technology, 73, 473-480.
[9] Ma, J., Sun, D.-W., Qu, J.-h., Liu, D., Pu, H., Gao, W.-h., Zeng, X.a. (2015). Applications of computer vision for assessing quality of agri-food products: a review of recent research advances. Food Science and Nutrition, 56(1): 113-127.
[10] Amza, C. G., Cicic, D. T. (2015). Industrial image processing using fuzzy-logic. Procedia Engineering, 100, 492-498.
[11] کارگذاری، م. (1386). بهینهسازی خشک کردن اسمزی هویج با استفاده از روش سطح پاسخ. پایاننامه دوره کارشناسی ارشد علوم و صنایع غذایی، دانشکده مهندسی و فناوری کشاورزی، دانشگاه تهران.
[12] فتاحی، س؛ طاهری گراوند، ا؛ شهبازی، ف. ا. (1396). تخمین تازگی گوشت مرغ مبتنی بر تکنیکهای پردازش تصویر و هوش مصنوعی. مهندسی بیوسیستم ایران، دوره 48 ، شماره 4، ص 503-491.
[13] جوادیکیا، ح؛ قاسمی ورنامخواستی، م؛ سبزی، س. (1396). تشخیص تازگی گوشت گوساله به کمک پردازش تصویر و سطح پاسخ. نشریه پژوهشهای علوم و صنایع غذایی ایران، جلد 13 ، شماره 2، ص261-251.
[15] Shi, Z., He, L. (2010). Application of neural networks in medical image processing, In: Proceedings of the 2nd International Symposium on Networking and Network Security, (pp. 2-4), Jinggangshan, China.
[16] García-Mateosa, G., Hernández-Hernándezc, J.L., Escarabajal-Henarejosb, D., Jaén-Terronesa, S. Molina-Martínez. J.M. (2015). Study and comparison of color models for automatic image analysis in irrigation management, applications, Agricultural Water Management, 151, 158–166.
[17] Zhou, X., Yuan, J. & Liu, H. (2015). A Traffic Light Recognition Algorithm Based On Compressive Tracking. International Journal of Hybrid Information Technology, 8(6), 323-332.
[18] Rotaru, C., Graf, T., & Zhang, J. (2008). Color image segmentation in HSI space for automotive applications. Journal of Real-Time Image Processing, 3, 311-322.
[19] Dowlati, M., Mohtasebi, S. S., de la Guardia, M. (2012). Application of machine-vision techniques to fish-quality assessment, Trends in Analytical Chemistry, 40, 168-179.
[20]Leon, K., Mery, D., Pedreschi, F., Leon, J. (2006). Color measurement in L*a*b* units from RGB digital image. Food Research International, 39 (10), 1084–1091.
[21] Forsyth, D., Ponce, J. (2003). Computer Vision: A Modern Approach. Prentice Hall, New Jersey.
Forsyth, D. A., & Ponce, J. (2003). A modern approach. Computer vision: a modern approach, 88.
[22] Hosseinpour, S., Rafiee, Sh., Mohtasebi, S.S., Aghbashlo, M. (2013). Application of computer vision technique for on-line monitoring of shrimp color changes during drying. Journal of Food Engineering 115 (1), 99–114.
[23] Dowlati, M., Mohtasebi, S. S., Omid, M., Razavi, S. H., Jamzad, M., & De La Guardia, M. (2013). Freshness assessment of gilthead sea bream (Sparus aurata) by machine vision based on gill and eye color changes. Journal of Food Engineering, 119(2), 277-287.
[24] Khulal, U., Zhao, J., Hu, W., & Chen, Q. (2016). Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms. Food chemistry, 197, 1191-1199.
[25] Montgomery, DC. Design and analysis of experiments. 2017. Jon wiley and sons.