Combined LF-NMR and Artificial Intelligence for Continuous Real-Time Monitoring of Carrot in Microwave Vacuum Drying

Abstract

In this paper, intelligent technology of combined low field NMR (LF-NMR) and back propagation artificial neural network (BP-ANN) was used to monitor moisture content in carrot during microwave vacuum drying. The relationship between different drying powers (200, 300, and 400 W) and NMR signals (A21, A22, A23, and Atotal) was investigated. Results show that as the drying time elapsed, the NMR signals of Atotal and A23 decrease all drying conditions, A21 and A22 tend to increase at high moisture content and then decrease, which is consistent with the state of water while changes during drying. NMR signals can be used as indicators for online monitoring of moisture and control of the drying process. With NMR signals as input variables, a BP-ANN model was built optimized by transfer function, training function, and the number of neurons to model the moisture content (output). Compared with a linear regression model and multiple linear regression model, the BP-ANN model with the topology of 4-25-1, transfer function of tansig and purelin, and training function of trainlm outperformed the fitting performance and accuracy. This shows that the combined approach of utilizing LF-NMR and BP-ANN has great potential in intelligent online monitoring and control applications for carrot drying.