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利用数值模拟和人工智能预测脱水洋葱的传质活化能和物理化学性质。

Predicting mass transfer activation energy and physicochemical properties of dried onion using numerical modeling and artificial intelligence.

作者信息

Liu Fangfang, El-Mesery Hany S, ElMesiry Ahmed H, Hu Zicheng, Salem Ali

机构信息

School of Mechanical and Electrical Engineering, Suqian University, Suqian, 223800, China.

School of Energy and Power Engineering, Jiangsu University, Zhenjiang, 212013, China.

出版信息

Sci Rep. 2025 Jun 3;15(1):19463. doi: 10.1038/s41598-025-04814-7.

DOI:10.1038/s41598-025-04814-7
PMID:40461803
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12134107/
Abstract

The quality of the onion slices was statistically evaluated based on the variables of drying conditions, considering the following characteristics: drying time, color, shrinkage, water activity, and rehydration ratio, critical parameters in evaluating food processing and preservation methods. The onion slices were subjected to drying under the ensuing settings: infrared powers of 1500, 3500, and 5500 W/m; airflow rates of 0.3, 0.7, and 1.0 m/s; and air temperatures of 40, 50, and 60 °C. The findings indicated that the drying time increased with air velocity to 0.7 and 1.0 m/s at a constant infrared intensity of 5500 W/m, resulting in a significant increase of 26 and 37%. The water activity values for the sample ranged from 0.40 to 0.49. The maximum vitamin C content was 24 mg/g, followed by an air temperature of 40 °C and 0.3 m/s air velocity under 1500 W/m. Conditions of 1.5 m/s, 40 °C, and 1500 W/m indicate the lowest rehydration ratio. The ANN model proved to be a robust tool for predicting and optimizing drying parameters, including drying duration, energy consumption, and quality. Additionally, it highlights the potential of advanced heating technologies to optimize food drying approaches, providing important insights for the food sector in its quest for increased efficiency.

摘要

基于干燥条件的变量,对洋葱片的质量进行了统计评估,考虑以下特征:干燥时间、颜色、收缩率、水分活度和复水率,这些都是评估食品加工和保存方法的关键参数。洋葱片在以下设置下进行干燥:红外功率为1500、3500和5500 W/m;气流速度为0.3、0.7和1.0 m/s;空气温度为40、50和60°C。研究结果表明,在红外强度恒定为5500 W/m的情况下,干燥时间随着空气流速增加到0.7和1.0 m/s而增加,显著增加了26%和37%。样品的水分活度值在0.40至0.49之间。最大维生素C含量为24 mg/g,其次是在1500 W/m下空气温度为40°C且空气流速为0.3 m/s的情况。1.5 m/s、40°C和1500 W/m的条件表明复水率最低。人工神经网络模型被证明是预测和优化干燥参数(包括干燥持续时间、能源消耗和质量)的强大工具。此外,它突出了先进加热技术在优化食品干燥方法方面的潜力,为食品行业提高效率提供了重要见解。

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