使用拉曼光谱和深度学习快速确定糖的总含量和抗氧化能力
Li Xiao1, Jinxin Liu1, Marti Z Hua1
1Department of Food Science and Agricultural Chemistry, Faculty of Agricultural and Environmental Sciences, McGill University, Sainte-Anne-de-Bellevue, QC H9X 3V9, Canada.
Food chemistry
|September 20, 2024
概括
拉曼光谱与深度学习相结合,准确地预测了糖的抗氧化能力. 便携式设备提供可靠的现场分析,与质量控制的桌面光谱仪性能相匹配.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 糖的抗氧化特性对其健康益处和质量至关重要.
- 确定抗氧化能力的传统方法耗时且资源密集.
研究的目的:
- 开发和验证一种快速,非破坏性的方法来评估糖的总含量 (TPC) 和抗氧化能力.
- 为了比较便携式和台式拉曼光谱的性能,再加上深度学习来进行此分析.
主要方法:
- 从36个不同颜色的糖样本中收集了360个拉曼光谱,使用便携式和长板光谱仪.
- 使用Folin-Ciocalteu,DPPH,ORAC和FRAP测定用于参考TPC和抗氧化能力测量.
- 开发了深度学习模型,根据光谱数据预测抗氧化剂概况.
主要成果:
- 深度学习模型使用便携式拉曼光谱实现了高精度 (R2 > 0.88) 和低误差 (RMSE 7.2-17.9%,MAE 5.2-13.1%) .
- 便携式拉曼光谱模型的性能与基板系统相当.
- 演示了模型在不同的糖颜色中预测抗氧化剂概况的能力.
结论:
- 与深度学习集成的便携式拉曼光谱技术提供了一个可行的工具,用于快速,现场的糖的抗氧化剂概况.
- 这种方法在质量控制和认证方面比传统的湿化学方法有了显著的进步.
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