整合干燥动力学,质量属性和机器学习,用于在温室太阳能干燥机中干燥红胡
Ghader Khaledifard1, Morteza Taki2, Rasoul Meamar Dastjerdi1
1Department of Agricultural Machinery and Mechanization Engineering, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, P.O. Box: 6341773637, Mollasani, Iran.
Scientific reports
|December 23, 2025
概括
温室太阳能干燥器 (GSD) 提高红干燥效率,并比开放式阳光或阴影方法更好地保存生物活性化合物. 机器学习准确地预测了干燥质量,优化了可持续的食品加工.
科学领域:
- 农业工程 农业工程
- 食品科学 食品科学 食品科学
- 可再生能源技术可再生能源技术
背景情况:
- 传统的干燥方法,如开放的阳光和阴影干燥,往往导致低效的水分去除和农产品中有价值的生物活性化合物的降解.
- 温室太阳能干燥器 (GSD) 技术提供了一个可控的环境,有可能提高干燥效率和产品质量.
- 使用机器学习的预测建模可以优化干燥过程并确保一致的产品质量.
研究的目的:
- 为了比较使用开放式阳光,阴影和温室太阳能干燥器 (GSD) 方法干燥的红的干燥性能,生物活性化合物保留和抗氧化活性.
- 在不同的干燥条件下模拟红的干燥动力学.
- 应用和验证机器学习技术,特别是高斯过程回归 (GPR),用于在GSD中干燥红胡时预测质量.
主要方法:
- 在受控的环境条件下使用三种方法试验干燥红:开放的阳光,阴影和GSD.
- 在干燥过程中监测湿度比,温度,太阳辐射和湿度.
- 使用Midilli和Kucuk模型和机器学习 (GPR) 对干燥动力学进行数学建模,用于用k-fold交叉验证进行质量预测.
主要成果:
- 该GSD方法表现出优越的干燥效率,导致与开放式阳光和阴影干燥相比,更快地去除水分.
- 在使用GSD方法干燥的胡中观察到总含量,总黄含量和抗氧化活性的更高保留率.
- 米迪利和库库克模型有效地描述了干燥行为,而GPR在预测GSD期间红胡质量方面取得了高准确性 (R2 = 0.98,MAPE = 4.86%).
结论:
- 温室太阳能干燥机 (GSD) 是干燥红的更有效的方法,提高了干燥性能并保持了关键的质量属性.
- 数学和机器学习模型为了解和优化红干燥过程提供了宝贵的工具.
- 将GSD技术与动态和机器学习建模集成,为开发可持续和智能食品干燥系统提供了一个有希望的方法.
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