Authors
Aijia Xiang, Xu Lin, Xinjie Yang, Hui Xu, Jie Pang, Xiaoe Chen, Zhiming Yan
Published in
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy. Volume 363. Issue Pt 1. Pages 128268. Jun 16, 2026. Epub Jun 16, 2026.
Abstract
In this study, a ratiometric fluorescence sensing strategy combined with deep learning was developed for the intelligent detection of tetracycline antibiotics (TCs) in food matrices. Blue-emitting and saffron yellow-emitting copper nanoclusters (Cu NCs) were synthesized using bovine serum albumin (BSA) and 2,3,5,6-tetrafluorothiophenol (TFTP) as ligands, respectively, and employed to construct a dual-emission ratiometric fluorescence probe (BSA/TFTP@Cu NCs). The probe exhibited distinct fluorescence response patterns toward tetracycline (TC), chlortetracycline (CTC) and doxycycline (DOX), achieving a detection limit as low as 9.26 nmol L-1. Under 302 nm UV illumination, visually distinguishable fluorescence color variations were observed for the three TCs. To enable intelligent analysis, a modified ResNet50 model incorporating multi-task learning and a progressive three-stage training strategy was developed. The model simultaneously achieved TCs classification and concentration prediction, attaining 100% classification accuracy and a concentration prediction accuracy exceeding 98% (R2 = 0.989). In the analysis of spiked milk, egg and honey samples, the recoveries of TCs ranged from 90.48% to 107.93% for the fluorescence method and 90.28% to 98.53% for the deep learning method. This study provides a novel strategy for the rapid, accurate and intelligent detection of antibiotic residues in food safety monitoring.
PMID:
42320161
Bibliographic data and abstract were imported from PubMed on 20 Jun 2026.
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