Combination of LF‐NMR and BP‐ANN to monitor the moisture content of rice during hot‐air drying

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 lowfield 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 Â 10 À6 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 ApplicationsThe 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.

Use of low‐field nuclear magnetic resonance for moisture variation analysis of fresh in‐shell peanut during different drying methods

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Effect of Direct-Contact Ultrasonic and Far Infrared Combined Drying on the Drying Characteristics and Quality of Ginger

“…LF-NMR (Low field nuclear magnetic resonance) technology was used to determine the changes in water migration of raw ginger slices [41]. In the drying process, the transverse relaxation time T 2 (transverse relaxation time) reflected the distribution and changes in the water content in different states [42], the smaller T (relaxation time) indicated the smaller degree of water freedom [43], indicating that water was more tightly bound to macromolecules in the environment in which it was located and less easily removed. The greater the T 2 , the greater the water freedom and the easier the removal [44].…”