Paper-based fluorescence sensor array with functionalized carbon quantum dots for bacterial discrimination using a machine learning algorithm

• Published in: Analytical and bioanalytical chemistry Vol. 416; no. 13; pp. 3139 - 3148
• Main Authors: Wang, Fangbin, Xiao, Minghui, Qi, Jing, Zhu, Liang
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024; Springer; Springer Nature B.V
• Subjects: Algorithms; Ampicillin; Analysis; Analytical Chemistry; Anti-Bacterial Agents - analysis; Anti-Bacterial Agents - pharmacology; Antibiotics; Bacteria; Bacteria - isolation & purification; Biochemistry; Biosensing Techniques - instrumentation; Biosensing Techniques - methods; Carbon; Carbon - chemistry; Carbon dots; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; cost effectiveness; Fabrication; Fluorescence; Food safety; Food Science; Gentamicin; Identification and classification; Inkjet printing; Laboratory Medicine; Learning algorithms; Machine Learning; mobile telephones; Monitoring/Environmental Analysis; Penicillin; Polymyxin B; printers; Printers (data processing); Quantum dots; Quantum Dots - chemistry; Research Paper; Sensor arrays; Sensors; species; Spectrometry, Fluorescence - methods; China; Bacterial discrimination; CQDs; Sensor array; Machine learning
• ISSN: 1618-2642; 1618-2650; 1618-2650
• DOI: 10.1007/s00216-024-05262-4

The rapid discrimination of bacteria is currently an emerging trend in the fields of food safety, medical detection, and environmental observation. Traditional methods often require lengthy culturing processes, specialized analytical equipment, and bacterial recognition receptors. In response to this need, we have developed a paper-based fluorescence sensor array platform for identifying different bacteria. The sensor array is based on three unique carbon quantum dots (CQDs) as sensing units, each modified with a different antibiotic (polymyxin B, ampicillin, and gentamicin). These antibiotic-modified CQDs can aggregate on the bacterial surface, triggering aggregation-induced fluorescence quenching. The sensor array exhibits varying fluorescent responses to different bacterial species. To achieve low-cost and portable detection, CQDs were formulated into fluorescent ink and used with an inkjet printer to manufacture paper-based sensor arrays. A smartphone was used to collect the responses generated by the bacteria and platform. Diverse machine learning algorithms were utilized to discriminate bacterial types. Our findings showcase the platform's remarkable capability to differentiate among five bacterial strains, within a detection range spanning from 1.0 × 10 3  CFU/mL to 1.0 × 10 7  CFU/mL. Its practicality is further validated through the accurate identification of blind bacterial samples. With its cost-effectiveness, ease of fabrication, and high degree of integration, this platform holds significant promise for on-site detection of diverse bacteria. Graphical abstract