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A Full-Color Carbon Quantum Dots Fluorescence Sensing Array Combined with Machine Learning for Rapid Bacterial
Lixin Kang1,2, Jia Wang1,2, Xianfeng Lin1,2
1State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, 214122, China.
Advanced Healthcare Materials
|August 18, 2025
Summary
A new fluorescence sensing array using carbon quantum dots (CQDs) enables rapid bacterial identification. This technology accurately detects and classifies common pathogens in food samples, offering a significant advancement for food safety.
Area of Science:
- Analytical Chemistry
- Materials Science
- Biotechnology
Background:
- Rapid bacterial identification is crucial for food safety, medical diagnostics, and environmental monitoring.
- Conventional methods are slow, labor-intensive, and require specialized equipment, hindering high-throughput applications.
- There is a need for faster, more efficient bacterial detection and classification techniques.
Purpose of the Study:
- To develop a novel multichannel fluorescence sensing array for rapid bacterial detection and classification.
- To synthesize water-soluble, full-color carbon quantum dots (CQDs) for the sensing array.
- To integrate the sensing array with machine learning algorithms for enhanced bacterial analysis.
Main Methods:
- Synthesis of water-soluble carbon quantum dots (CQDs) using an acid reagent engineering strategy.
- Development of a multichannel fluorescence sensing array exploiting differential CQD responses to bacterial properties.
- Integration of machine learning algorithms for data analysis and bacterial classification.
- Testing the array in pure cultures and a complex food matrix (pork).
Main Results:
- Successful synthesis of CQDs with emission wavelengths from 422 to 679 nm.
- 100% accurate identification of five common pathogenic bacteria in controlled experiments.
- Accurate differentiation, quantification, and identification of mixed bacterial populations in a pork matrix (>93% accuracy).
- Demonstrated multidimensional fluorescence signal acquisition based on bacterial characteristics.
Conclusions:
- The developed CQD-based fluorescence sensing array provides a versatile and effective platform for rapid bacterial detection and classification.
- This technology shows significant potential for applications in food safety, diagnostics, and environmental monitoring.
- The combination of CQDs and machine learning offers a powerful approach for complex bacterial analysis.
