Machine Learning-Enabled Multidimensional Ratiometric Sensor Array for Accurate Discrimination and Quantification of Tetracyclines

ratiometric fingerprints under single-wavelength excitation, combined with linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA), the sensor effectively discriminates tetracycline (TC), oxytetracycline (OTC), chlortetracycline (CTC), and doxycycline (DOX) across wide concentration gradients in both single-component and binary mixture systems, with discrimination sensitivity surpassing that of existing sensor arrays. The detection limits for the four TCs are 0.136 μM (OTC), 0.021 μM (DOX), 0.039 μM (TC), and 0.247 μM (CTC). The sensor demonstrates excellent antimatrix interference capability (matrix effect values from 0.92 to 1.04) and quantitative accuracy in milk samples (recoveries ranging from 95.40% ± 1.68% to 109.40% ± 1.12%). The sensor array also exhibits outstanding TCs discrimination capability in milk samples, confirming its robustness and reliability for high-throughput screening in complex food matrices.