Parnian Pezeshki - Department of Nutrition Sciences, Varastegan Institute for Medical Sciences, Mashhad, Iran
Mohammadbagher Habibi Najafi - Departments of Food Science and Technology, Faculty of Agriculture, Ferdowsi University of Mashhad,Iran
Masoud Yavarmanesh - Departments of Food Science and Technology, Faculty of Agriculture, Ferdowsi University of Mashhad,Iran
Mohebbat Mohebi - Departments of Food Science and Technology, Faculty of Agriculture, Ferdowsi University of Mashhad,Iran

Background and Aim: F-RNA coliphages, are part of the poultry and cattle’s gut flora and likely to be deposited on meat along with other enteric organisms during carcass dressing and processing. Methods: Adaptive neuro-fuzzy inference system (ANFIS) and fuzzy logic models were used to predict the effect of storage condition and meat aging on MS2 bacteriophage survival as a surrogate of enteric viruses on lamb and chicken, respectively. ANFIS model as a function of meat sources (lamb and chicken), MS2 concentration (10, 103 and 105 pfu) and storage time (6 months for freezing and 7 weeks for cold storage) was developed. Input parameters of the fuzzy logic system were as follows; MS2 concentration, pH, water holding capacity, myofibril fragmentation index and total count of mesophilic bacteria. This input data was used to classify MS2 bacteriophage survival rate according to the following scale; low, medium and highResults: The results showed Takagi-Sugeno-Kang (TSK) fuzzy model, Bell membership function and Hyperbolic tangent transfer function with Levenberg-Marquardt and Momentum algorithm (r=0.99) were the best predicting model of MS2 bacteriophage survival rate in cold storage and freezing , respectively. The overall agreement between fuzzy logic system predictions and experimental data to investigate the effect of meat aging on MS2 bacteriophage survival rate in lamb (r=0.915) and chicken (r=0.971) was very good and reliable at P 0.05.Conclusion: The models that were applied in this study capable to produce accurate results for predicting survival of MS2 bacteriophage that is necessary step in order to predict the enteric viruses on meat and protect public health

MS2 bacteriophage; Predictive modeling; Lamb; Chicken; Fuzzy logic; ANFIS