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<p>Flavonoids are a class of polyphenolic compounds which are widely distributed in plants, and its excessive intake may cause adverse reactions. Traditional detection methods, which rely on costly instruments and consume a large amount of human resources, are difficult to use for on-site detection. In this study, a fluorescence sensing platform based on nitrogen-doped carbon dots (N-CDs) was constructed by a simple and easy-to-operate microwave method. Under the optimal detection conditions, the N-CDs fluorescent probe had a wide detection range (20&#xa0;mg/L–100&#xa0;mg/L) and a low detection limit (LOD) (myricetin 6.27&#xa0;mg/L, morin 3.79&#xa0;mg/L, kaempferol 3.74&#xa0;mg/L). A paper sensor based on N-CDs was designed to realize the semiquantitative visual analysis of flavonoids. In addition, we have improved the detection performance of kaempferol by smartphone-assisted fluorescence detection, with the LOD of 1.45&#xa0;mg/L. In order to further overcome the limitations of smartphone-assisted detection method on smartphone models, we integrated the Faster Region-based Convolutional Neural Networks (Faster R-CNN) target detection algorithm and machine learning (ML) regression algorithm. Consequently, a web-based concentration detection system for flavonoids was designed, establishing a portable intelligent detection platform.</p> Graphical abstract <p></p>

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Nitrogen-doped carbon dots as fluorescent probes for a web system-assisted intelligent detection model for flavonoids

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Yilin Wang,
  • Jingwei Zhao,
  • Shengxiao Gao,
  • Si-Si Zhao,
  • Xiaoqi Li,
  • Xue Chen,
  • Hang Zhang,
  • Tianyi Tian,
  • Zhen Zhao

摘要

Flavonoids are a class of polyphenolic compounds which are widely distributed in plants, and its excessive intake may cause adverse reactions. Traditional detection methods, which rely on costly instruments and consume a large amount of human resources, are difficult to use for on-site detection. In this study, a fluorescence sensing platform based on nitrogen-doped carbon dots (N-CDs) was constructed by a simple and easy-to-operate microwave method. Under the optimal detection conditions, the N-CDs fluorescent probe had a wide detection range (20 mg/L–100 mg/L) and a low detection limit (LOD) (myricetin 6.27 mg/L, morin 3.79 mg/L, kaempferol 3.74 mg/L). A paper sensor based on N-CDs was designed to realize the semiquantitative visual analysis of flavonoids. In addition, we have improved the detection performance of kaempferol by smartphone-assisted fluorescence detection, with the LOD of 1.45 mg/L. In order to further overcome the limitations of smartphone-assisted detection method on smartphone models, we integrated the Faster Region-based Convolutional Neural Networks (Faster R-CNN) target detection algorithm and machine learning (ML) regression algorithm. Consequently, a web-based concentration detection system for flavonoids was designed, establishing a portable intelligent detection platform.

Graphical abstract