High-Throughput Recognition and Detection of Multiclass Antibiotics Based on Fluorescence-Ultraviolet Dual-Signal CQDs Sensor Array

-phenylenediamine and 2,2'-Azobis(2,4-dimethylvaleronitrile) as precursors via microwave-assisted pyrolysis, and two types of CQDs with distinct fluorescent properties (DIW-CQDs and FA-CQDs) were prepared by dispersion in deionized water (DIW) and formic acid (FA), respectively. S1-CQDs were synthesized by compositing CQDs with Rhodamine B (RhB), exhibiting dual-emission fluorescence properties. S2-CQDs were obtained by blending DIW-CQDs and FA-CQDs. A single-component dual-channel sensor array was constructed using S1-CQDs as the sensing unit. The array collect the signals of ultraviolet (UV) and fluorescence, enabling the discrimination and detection of fluoroquinolone antibiotics (norfloxacin (NOR), ciprofloxacin (CIP), and ofloxacin (OFX)). Furthermore, a two-component four-channel sensor array was developed by employing S1-CQDs and S2-CQDs as sensing units, generating four distinct signals for the discrimination and detection of three antibiotic categories: tetracycline (TC), cefixime (CFM), and fluoroquinolones (NOR, CIP, and OFX). Combined with pattern recognition methods including linear discriminant analysis (LDA), hierarchical cluster analysis (HCA), and principal component analysis (PCA), the array achieved 100% accurate identification and quantitative detection of five antibiotics, with detection limits ranging from 3.9-14.0 nM. The LDA score plots revealed well-separated clusters for single antibiotics and their binary, ternary, and quaternary mixtures with intercluster Euclidean distances exceeding 3.0. This work provides a fluorescence/ultraviolet dual-signal sensor array strategy coupled with machine learning-assisted pattern recognition for efficient and reliable antibiotic analysis in complex matrices.