10.57647/jnsc.2026.1604.16

A Machine Learning-assisted Smartphone-based Method for Rapid Quantification of Total Flavonoids by Quantum Dots

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  • Ying Chen1,
  • Yiyu Qi1,
  • Ao Qian1,
  • Ao Qian1,
  • Jie Li1,
  • Jiaqi Guo2,
  • Yuchen He1,
  • Hassan Algadi3,
  • Juanna Ren4,
  • Bingnan Yuan1,
  • Zhanhu Guo5,
  • Yi Kuang1,
  1. College of Chemistry and Materials Engineering, Zhejiang A&F University, Hangzhou 311300, PR China
  2. Health Science Center, Ningbo University, Ningbo 315211, Zhejiang, PR China
  3. Department of Electrical Engineering, Faculty of Engineering, Najran University, Najran, 11001, Saudi Arabia
  4. College of Materials Science & Engineering, Taiyuan University of Science & Technology, Taiyuan 030024, PR China
  5. Department of Mechanical and Construction Engineering, Northumbria University, Newcastle, Upon Tyne, NE1 8ST, UK

Received: 19-10-2025

Revised: 31-01-2026

Accepted: 05-04-2025

Published in Issue 31-08-2026

Copyright (c) 2026 Ying Chen, Yiyu Qi, Ao Qian, Ao Qian, Jie Li, Jiaqi Guo, Yuchen He, Hassan Algadi, Juanna Ren, Bingnan Yuan, Zhanhu Guo, Yi Kuang (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Chen, Y., Qi, Y., Qian, A., Qian, A., Li, J., Guo, J., He, Y., Algadi, H., Ren, J., Yuan, B., Guo, Z., & Kuang, Y. (2026). A Machine Learning-assisted Smartphone-based Method for Rapid Quantification of Total Flavonoids by Quantum Dots. Journal of Nanostructure in Chemistry, 16(4 (August 2026). https://doi.org/10.57647/jnsc.2026.1604.16
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Abstract

This study presents a rapid and instrument-free method for the quantification of total flavonoids, utilizing smartphone imaging and lightweight machine learning techniques. The methodology involves capturing fluorescence images of zinc-based quantum dots that are quenched by varying concentrations of quercetin, a representative flavonoid, using a smartphone camera. A pre-trained convolutional neural network (CNN) model was developed, which can be fine-tuned with a minimal dataset to achieve accurate quantification of total flavonoids in specific plant samples. Using Euphorbia humifusa as a model plant, the method demonstrated a linear detection range of 5–500 μg/mL, with quantification errors ranging from 0.1 to 10 μg/mL. Compared to conventional methods, the detection limit was improved by a factor of 1.6, and the upper detection limit was also extended by a factor of 1.6. This approach provides simplicity, cost-effectiveness, and independence from specialized instruments, thereby offering a promising solution for on-site rapid detection of total flavonoids in plants.

Keywords

  • Convolutional neural network,
  • Machine learning,
  • Quantum dots,
  • Rapid quantification,
  • Smartphone-based detection
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