Study On The Characteristics Prediction Model Of Hot-air Drying Mushrooms And Optimization Of Drying Process Parameters

In order to improve the quality of dried mushrooms,experiments on fresh mushrooms under different hot-air drying parameters are carried out.The influence of relative humidity,air temperature,wind speed and unit load on drying characteristics and drying quality characteristics is analyzed.An artificial neural network model for predicting the moisture ratio is established and compared with the classical drying kinetic model.The response surface method is used to optimize the hot-air drying process parameters of mushrooms.The experimental results show that the drying time decreases with the decrease of relative humidity,the drying rate increases with the decrease of relative humidity,and the effective water diffusion coefficient decreases with the increase of relative humidity.Crude fat content,total sugar content,water-soluble protein content,color difference,rehydration ratio,and hardness decrease with the increase of relative humidity.When the relative humidity is 25%,the shortest time is only 9.5 hours.On this basis,when the relative humidity is 40%,the drying time is 1.31 times the benchmark.Fitting the drying kinetic model with experimental data,and it is found that the Modifited Page model is the most suitable drying kinetic model for mushrooms.An artificial neural network model is established based on particle swarm optimization,and the predicted values of the artificial neural network model and the Modifed Page model are compared with the experimental results.It is found that the average relative error between the predicted value of the artificial neural network model and the experimental value is 5.57 %,which is better than the 16.57% average relative error of the Modifited Page model.This shows that the established artificial neural network model is more suitable for the prediction of the moisture ratio of the hot-air drying of mushrooms.Taking dried mushroom color difference,water-soluble protein content and drying time as optimization targets,relative humidity,wind speed and unit load as process parameter variables,the entropy method is used to comprehensively weight the optimization targets,and the response surface method is used to establish a comprehensive predictive model of scoring.When the comprehensive score is the best,the color difference of dried mushrooms is 3.58,the water-soluble protein content is 46.39 mg/g,and the drying time is 12.5 hours.Corresponding to the drying conditions of 50 ℃,the best drying process parameters are 27.1% relative humidity,wind speed 5 m/s,and unit load 4 kg/m2.Compared with the experimental results,the model prediction value of the optimization target has an average relative error of 6.6% and a maximum relative error of 7.4%.It shows that the comprehensive scoring prediction model based on response surface method can be used in the hot-air drying process of mushrooms.