The research aimed to create a shelf life prediction model for Trachinotus ovatus in different freezing temperatures by using back propagation(BP)neural network model.The pH,total volatile basic nitrogen(TVB-N),thioba...The research aimed to create a shelf life prediction model for Trachinotus ovatus in different freezing temperatures by using back propagation(BP)neural network model.The pH,total volatile basic nitrogen(TVB-N),thiobarbituric acid(TBA),water retention(water holding capacity[WHC];cooking loss),and sensory evaluation were measured under 266 K,255 K,243 K,233 K,and 218 K temperatures.The results of TVB-N and water retention during 266 K,255 K,233 K,and 218 K conditions were selected to build a BP neural network model and verify the model at 243 K.Results indicated that low temperatures retarded the rise of pH,TVB-N,and TBA values,improving water retention capacity of Trachinotus ovatus.The BP neural network model had high regression coefficients(r2:0.8642-0.9904),low mean square error(MES:0.1658-1.7882),and relative error within 10%and could accurately predict the quality change of Trachinotus ovatus under the freezing temperatures of 266 K-218 K.Therefore,(BP)neural network model has great potential in predicting the shelf life of Trachinotus ovatus in frozen storage.展开更多
基金supported by China Agricultural Research System(CARS-47-G26)National Key R&D Program of China(2019YFD0901602)Ability promotion project of Shanghai Municipal Science and Technology Commission Engineering Center(19DZ2284000).
文摘The research aimed to create a shelf life prediction model for Trachinotus ovatus in different freezing temperatures by using back propagation(BP)neural network model.The pH,total volatile basic nitrogen(TVB-N),thiobarbituric acid(TBA),water retention(water holding capacity[WHC];cooking loss),and sensory evaluation were measured under 266 K,255 K,243 K,233 K,and 218 K temperatures.The results of TVB-N and water retention during 266 K,255 K,233 K,and 218 K conditions were selected to build a BP neural network model and verify the model at 243 K.Results indicated that low temperatures retarded the rise of pH,TVB-N,and TBA values,improving water retention capacity of Trachinotus ovatus.The BP neural network model had high regression coefficients(r2:0.8642-0.9904),low mean square error(MES:0.1658-1.7882),and relative error within 10%and could accurately predict the quality change of Trachinotus ovatus under the freezing temperatures of 266 K-218 K.Therefore,(BP)neural network model has great potential in predicting the shelf life of Trachinotus ovatus in frozen storage.
