The establishment of Arrhenius prediction model for tomato quality under extreme meteorological temperatures
Objective:A coupled model for predicting tomato quality during short-term storage and transportation under extreme temperatures,utilizing the Arrhenius equation in conjunction with meteorological temperature data,to forecast the quality changes in tomatoes during the short-term storage and transportation processes under extreme weather conditions.Methods:Analyzing the meteorological temperature data for the year 2020 in Weifang,Baoding,and Daxing,7 temperature points were selected to simulate the range of temperature variations for short-term storage and transportation of tomatoes.By utilizing the Arrhenius equation and integrating temperature with weight loss rate,hardness,color difference(△E),and sensory evaluation scores(SE),a coupled model was developed for predicting tomato quality.The model was validated by using temperatures of-10 ℃ and 12 ℃.Results:Within 48 hours of storage and transportation,Loss rate and △E of tomatoes gradually increased,while evaluation scores(SE)and hardness decreased gradually.The quality prediction coupling model based on the Arrhenius equation combined with meteorological temperature data was constructed.Under storage conditions from 0 to 36 ℃,changes in loss rate and SE were fitted with zero-order reactions,while changes in hardness and △E were fitted with first-order and half-order reactions,respectively.For storage conditions from-15 to 0 ℃,zero-order reaction fits were applied to model the changes in SE,△E,weight loss rate,and hardness.Validation of the predictive model revealed that,under 12 ℃ storage conditions,the relative errors for tomato weight loss rate,hardness,and SE were within 15％,except for the 48 hour prediction.Under-10 ℃ storage conditions,the relative errors for tomato hardness and SE were within 15％,excluding the 48 hour prediction.Conclusion:The coupled model for tomato quality prediction,constructed by integrating the Arrhenius equation with extreme meteorological temperature data,proves to be effective in forecasting the quality of tomatoes under extreme temperature conditions.
万方数据
维普
