Application of an artificial neural network model to predict the change of moisture during drying of sturgeon bone marrow

Abstract

In the experiment of this article, the artificial neural network (ANN) was used to establish the sturgeon bone marrow drying model. Further, the effects of different temperatures (40, 60, and 80°C), humidities (0, 20, and 40%), and air velocities (8, 16, and 25 m/s) on the drying characteristics of sturgeon bone marrow were stud-ied. The studies had shown that with the increase of drying temperature, the acceleration of air velocity, and the decrease of humidity, the sturgeon bone marrow can be dried in the shortest period of 100 min. This study used ANN to feasibly predict dried sturgeon bone marrow moisture ratio, based on the time, temperature, humidity, and air velocity drying inputs. The results revealed that 11 hidden neurons were selected as the best configuration to predict the moisture ratio. This network was able to predict moisture ratio with R value 0.996. This model correctly predicted the optimal drying conditions and established that temperature is the single most significant factor in determining the drying time of sturgeon bone marrow. It is expected that this system will have broader application in other food drying requirements.

Design and Experiment of Combined Infrared and Hot-Air Dryer Based on Temperature and Humidity Control with Sea Buckthorn (Hippophae rhamnoides L.)

“…However, rapid moisture evaporation from a material’s surface during drying may cause the material’s surface to harden and prevent water from evaporating during the drying stage. Recent developments in drying technology have led to improvements in temperature and humidity control [ 11 ]. A high relative humidity during the initial drying stage causes the internal temperature of a material to rapidly increase.…”