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Updated: Feb 13, 2026

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通过基于碳量子点的色度传感器阵列和计算机视觉识别Anhua黑茶的智能存储年
Wu Wang1, Ye He2, Wenjie Zhou1
1Hunan Key Lab of Biomedical Materials and Devices, College of Biological and Medical Engineering, Hunan University of Technology, Zhuzhou 412007, PR China.
Food chemistry
|February 11, 2026
概括
这项研究引入了具有深度学习的色度传感器阵列 (CSA),以快速识别Anhua深茶储存年. 这种新的方法准确地验证了茶叶的真实性,有助于食品质量控制.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 材料科学 材料科学 材料科学
背景情况:
- 安华黑茶的价值随着年龄的增长而增加,但很难确定其储存年份.
- 需要快速准确的方法来验证茶叶的真实性.
研究的目的:
- 开发一种快速,具有成本效益的方法来识别安华黑茶的储存年.
- 将色度传感器阵列 (CSA) 与深度学习相结合,用于茶叶认证.
主要方法:
- 使用五个碳量子点 (CQD) 的CSA被设计用于检测茶叶成分.
- 在紫外线下的智能手机成像捕获了颜色变化.
- 深度学习模型 (ResNet18,VGG16,DenseNet121,MobileNetV3-Small,EfficientNet B0) 分析了这些色度数据.
主要成果:
- 在识别存储年份方面,ResNet18取得了最高的准确性 (92.4%).
- 基于CSA的深度学习方法显著优于传统方法 (PLS-DA:57.6%,PCA-LDA:63.6%).
- 为非专家操作开发了一个用户友好的界面.
结论:
- 开发的CSA与深度学习相结合,为安华深茶认证提供了一个全新的,可访问的平台.
- 这项技术在现实世界食品质量控制和真实性验证方面具有重大潜力.
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