Combination of LF-NMR and BP-ANN to monitor water states of typical fruits and vegetables during microwave vacuum drying

Abstract To set up a rapid real-time nondestructive detection of moisture content, this paper reported the results of a combination of LF-NMR and BP-ANN to monitor the relationship between drying parameters and state of water under different microwave vacuum drying conditions. Three kinds of materials, carrot (fruit), banana (vegetable) and pleurotus eryngii (edible fungus), were tested in the experiment of applicability. The resulted showed that the information of Atotal and T23 can be used to analyze the drying behavior and the information of A20, A21 and A22 can be used as the fingerprint characteristics of material discrimination. Three classic models (PLS, SVM and BP-ANN) were compared to study the prediction ability of moisture content with the inputs of A20, A21, A22, A23 and Atotal. The performance of BP-ANN model was the best. Although the BP-ANN model of mixed species was not as good as the BP-ANN model of single fruit or vegetable, it still had excellent predictive performances with R2 0.9969 and RMSE 0.0184 to meet the needs of current industry and production.