Sensitive Fluorescence Detection of Metronidazole Residues in Traditional Dairy Products Using Green-Synthesized Carbon Quantum Dots from Rosa canina: Combining Experimental Design and Machine Learning for Food Safety

In this study, CQDs were synthesized via a green hydrothermal method using Rosa Canina as a natural carbon source. The structural and optical features of the CQDs were analyzed using fluorescence spectroscopy, FTIR, SEM, EDX, and elemental mapping. The nanocomposite exhibited strong fluorescence emission, enabling sensitive detection of metronidazole (MNZ) through a fluorescence quenching mechanism. Sensor parameters were optimized with Design of Experiments (DoE), yielding a linear detection range of 5.0–400.0 µM, with a limit of detection of 2.24 µM and a limit of quantification of 7.39 µM. The sensor showed good repeatability (RSD = 3.38%, n = 12) and reproducibility (RSD = 3.47%). Machine learning was employed to improve predictive accuracy and data interpretation. The practical applicability was confirmed by the successful detection of MNZ in real dairy samples, with minimal matrix interference and satisfactory recovery.