茶叶质量的智能评估:机器学习辅助的SERS用于白茶年鉴认证和等级分类
Qian Ye1, Xianjin Zhang1, Yuanyuan Yao1
1Jiaxing Key Laboratory of Molecular Recognition and Sensing, Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems, College of Biological, Chemical Sciences and Engineering, Jiaxing University, Jiaxing, Zhejiang 314001, China.
Food chemistry
|January 24, 2026
概括
这项研究介绍了一个智能平台,使用表面增强的拉曼散射和机器学习来快速分析白茶. 它准确地分类了茶叶年份,并量化了表甲基酸盐 (EGCG) 含量,建立了科学质量标准.
科学领域:
- 分析化学 分析化学
- 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:
- 传统的茶叶感官评估缺乏科学严格性,原因是主观的可变性和无法量化生物活性化合物.
- 建立可验证的茶叶质量标准,特别是白茶,对于产业和消费者信任至关重要.
研究的目的:
- 开发一种快速,定量和科学可验证的白茶分析方法.
- 整合表面增强的拉曼散射 (SERS) 与机器学习 (ML) 进行茶叶质量评估.
- 为不同的白茶类型建立EGCG含量分级层次结构.
主要方法:
- 使用银纳米珊瑚基板用于SERS信号增强.
- 使用便携式785nm拉曼光谱仪进行全频谱数据采集.
- 开发了一种混合ML模型,将随机森林用于分类和梯度增强回归器用于定量化.
主要成果:
- 在使用Random Forest进行年份分类时,获得了96%的准确性.
- 使用梯度增强回归器,高精度 (R2 = 0.98) 的量化表甲基酸盐 (EGCG).
- 建立了一个一致的EGCG分级层次结构:阴 > 穆丹 > 贡梅 > 舒梅.
结论:
- 开发的智能平台为白茶分析提供了快速 (20分钟) 和客观的方法.
- 这种方法克服了传统感官评估的局限性,提供了定量生物活性标记数据.
- 已建立的EGCG分级层次与传统评估保持一致,同时提供科学验证.
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