整合一个卷积神经网络和多头注意力与长期短期记忆,用于干燥过程中的实时控制:尤巴 (豆腐皮肤) 的案例研究
Jiale Guo1, Jie Wu1, Lixuan Zhang2
1College of Engineering, China Agricultural University, 17 Qinghua Donglu, Beijing 100083, China.
Foods (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了使用CNN-LSTM-MHA网络的智能干燥系统,以提高农产品质量. 先进的模型提高了干燥速度和产品特性,大大缩短了加工时间.
科学领域:
- 农业工程 农业工程
- 食品科学与技术 食品科学与技术
- 食品加工中的人工智能
背景情况:
- 优化农产品的干燥速度 (DR) 和干燥后质量至关重要.
- 现有的干燥方法在平衡干燥速度与产品质量保护方面经常面临挑战.
研究的目的:
- 使用CNN-LSTM-MHA网络开发和验证智能热空气干燥 (HAD) 系统.
- 为了提高 DR,收缩率 (SR) 和总色差 (ΔE) 等干燥性能的预测准确度.
- 调查智能温度控制对尤巴品质属性的影响.
主要方法:
- 卷积神经网络 (CNN),多头注意力 (MHA) 和长短期记忆 (LSTM) 的整合用于预测建模.
- 开发一个具有智能温度控制系统的新型HAD场景.
- 使用温度敏感的尤巴进行实验验证,将智能干燥与固定温度方法进行比较.
主要成果:
- 美国有线电视新闻网-LSTM-MHA网络显示出优异的预测准确度 (R2: 0.9855-0.9999,RMSE: 0.0001-0.0099,MAE: 0.0001-0.0120).
- 与固定的温度方法相比,智能干燥显著减少了干燥时间.
- 提高了尤巴的质量,包括改进了SR,颜色,再水分比 (RR),质地,蛋白质,脂肪含量和微观结构.
结论:
- 基于CNN-LSTM-MHA的智能干燥系统为高效和高质量的尤巴加工提供了一个可行的策略.
- 这种方法为优化其他食品干燥应用提供了宝贵的见解.
- 智能控制系统可以有效地管理干燥过程中的动态材料特性,以改善结果.
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