BP Neural Network Modeling to Predict Moisture Content of Grapes after Air Impingement Drying
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Abstract:
The effect of different blanching pretreatment times (30, 60, 90 and 120 s) and drying temperatures (55 ℃, 60 ℃, 65 ℃, and 70 ℃) on the drying rate and drying time of grapes was investigated, and a BP neural network model was established. This network used an architecture of ‘3-7-1’, which includes input layers of blanching time, drying temperature and drying time, seven hidden layer variables, and a single output layer of moisture content. The results provided a mechanism to predict the moisture content of grapes under air impingement drying. The results demonstrated that blanching pretreatment time and drying temperature showed a significant impact on drying rate and longer blanching time and higher drying temperature resulted in a higher drying rate. The BP neural network model was configured for finite iteration calculation with Levenberg-Marquardt (LM) algorithm as the training function and tansig-purelin as the network transfer function. The correlation coefficient (R2) and root mean squared error (RMSE) between the predicted and measured values were 0.9915 and 0.03376, respectively.
