基于深度学习的多式融合,用于使用高光谱成像和近红外光谱学预测糊的质量
Mengmeng Li1, Lifei Lai1, Jinjing Yuan1
1College of Food and Bioengineering, Xihua University, Chengdu 610039, China.
Food chemistry
|July 31, 2025
概括
一个新的深度学习系统使用高光谱成像和光谱学非破坏性地评估粉质量. 这种智能多式联络方法可以在发酵过程中快速实时监控质量.
科学领域:
- 食品科学与技术 食品科学与技术
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 粉质量评估传统上依赖于破坏性方法.
- 精确的,实时监测糊发酵对于产品的一致性和安全至关重要.
- 开发非破坏性技术对于食品加工中高效的质量控制至关重要.
研究的目的:
- 开发一种智能多式联运系统,用于对粉质量进行非破坏性评估.
- 将高光谱成像 (HSI) 和近红外光谱 (NIRS) 的数据与物理化学指标合并.
- 建立快速检测框架,在粉发酵过程中实时监测质量.
主要方法:
- 使用混合卷积神经网络-长短期记忆模型利用深度学习.
- 采用了色彩,光谱和物理化学数据的特征级融合.
- 应用于数据预处理的乘法分散校正和特征选择的非信息变量消除.
- 实现了Mixup增强,以扩展数据集并减轻深度学习过拟合.
主要成果:
- 多式联网系统在质量参数方面的预测性能非常出色 (测试组R2 = 0.95540.9826).
- 多倍散射校正和非信息变量消除在数据预处理和特征选择方面被证明是有效的.
- 与其他预测模型相比,混合深度学习模型显示出更高的准确性.
结论:
- 开发的系统提供了第一个非破坏性和快速检测框架,用于粉发酵.
- 这种智能多式联运方式为实时质量监测提供了重要的技术支持.
- 该研究强调了将HSI,NIRS和深度学习整合到食品质量评估中的潜力.
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