Combination of <scp>LF‐NMR</scp> and <scp>BP‐ANN</scp> to monitor the moisture content of rice during hot‐air drying
Hongchao Wang, Gang Che, Lin Wan, Xin Wang, Hao Tang
Abstract
Abstract In this study, a rapid real‐time nondestructive method for detecting the moisture content of rice during hot‐air drying was investigated. Intelligent techniques of low‐field nuclear magnetic resonance (LF‐NMR) and back propagation artificial neural network (BP‐ANN) were applied to monitor the moisture content of rice. The effect of different hot‐air temperatures (35, 45, 55, and 65°C) on the moisture content and water migration within rice was studied. The results showed that the drying temperature promoted the diffusion and transfer of water within the rice, and was positively proportional to the drying rate. The binding energy of the different states of water within rice increased with the drying process, and the variation in relaxation time and peak area was consistent for each stage at different temperatures. In addition, the amount of LF‐NMR signals was used as an indicator to build a predictive model for the moisture content of rice during hot‐air drying. A BP‐ANN prediction model optimized by transfer function, training function and number of neurons was used to monitor the moisture content of rice using the amount of LF‐NMR signals of different states of water as input variables. The optimized neural network model had the excellent predictive ability with an MSE of 6.02 × 10 −6 and R 2 of 0.996. These results provide a reference for combining LF‐NMR and BP‐ANN in the application of intelligent online monitoring of hot‐air drying of rice. Practical Applications The monitoring of moisture content during hot‐air drying of rice is an essential parameter for optimizing the drying process. The combined approach of LF‐NMR and BP‐ANN for rapid real‐time nondestructive monitoring is well suited to hot‐air drying of rice, allowing for improved product quality and operational processes. In addition, the model developed in this study has the good predictive performance to meet the current industry and production needs, providing new research ideas and technical references for the optimization of the drying process of rice.