使用高光谱成像技术和深度学习用于质量参数的热空气干燥的评价和过程监测
Quancheng Liu1, Xinna Jiang1, Fan Wang1
1School of Technology, Beijing Forestry University, Beijing 100083, China; State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China; Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China.
Food chemistry
|December 8, 2024
概括
超光谱成像和深度学习模型在热空气干燥过程中精确监测了朱的质量. 这种非破坏性方法可视化可溶固体含量,酸度,湿度和硬度,以改善水果加工.
科学领域:
- 农业工程 农业工程
- 食品科学 食品科学 食品科学
- 频谱学是一种光谱学.
背景情况:
- 在水果加工中,有效的质量控制至关重要,特别是在干燥过程中.
- 需要非破坏性方法来实时监测水果的质量属性.
研究的目的:
- 在热空气干燥过程中应用超光谱成像和深度学习来非破坏性监测中的可溶性固体含量 (SSC),可定位酸度 (TA),水分和硬度.
- 开发和评估一个深度学习模型 (CNN_BiLSTM_SE) 来预测这些质量参数.
主要方法:
- 使用高光谱成像技术收集了在55°C,60°C和65°C干燥的朱树的数据.
- 开发了一个深度学习模型,CNN_BiLSTM_SE (卷积神经网络-双向长期短期记忆-挤压和激发).
- 使用各种预处理技术 (MSC,基线,MSC_1st) 将模型性能与部分最小平方回归 (PLSR) 和支向量回归 (SVR) 进行了比较.
主要成果:
- 与PLSR和SVR相比,优化的CNN_BiLSTM_SE模型在预测朱果质量属性方面表现出卓越的性能.
- 开发的模型使得质量属性的像素级预测成为可能.
- 为不同的干燥阶段生成SSC,TA,湿度和硬度的时空分布图.
结论:
- 超光谱成像与CNN_BiLSTM_SE深度学习模型相结合,提供了一种强大的,非破坏性的方法,用于在热空气干燥期间监测朱质量.
- 这项技术有助于实时质量评估和可视化,有助于优化水果加工并确保产品质量.
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