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

Caiyan Jiang, Shan Shang, Jie Zheng, Baoshang Fu, Minqiang Guo, Pengbo Shen, Pengfei Jiang
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引用次数: 1

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.
应用人工神经网络模型预测鲟骨髓干燥过程中水分变化
在本文的实验中,采用人工神经网络(ANN)建立了鲟鱼骨髓干燥模型。此外,还研究了不同温度(40、60和80°C)、湿度(0、20和40%)和风速(8、16和25 m/s)对鲟鱼骨髓干燥特性的影响。研究表明,随着干燥温度的升高、风速的加快和湿度的降低,鲟鱼骨髓在最短的100 min内即可完成干燥。本研究基于干燥时间、温度、湿度和风速的输入,利用人工神经网络对干燥后的鲟鱼骨髓含水率进行了可行的预测。结果表明,11个隐藏神经元被选为预测水分比的最佳配置。该网络预测水分比的R值为0.996。该模型正确地预测了最佳干燥条件,并确定温度是决定鲟鱼骨髓干燥时间的最重要因素。预计该系统将在其他食品干燥要求中有更广泛的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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