Exploration of the fluorine−fluorine interaction mechanism in fluoroquinolone antibiotics recognition and ciprofloxacin detection on the basis of fluorine−doped carbon quantum dots and machine learning

The uncontrolled use of antibiotics poses a significant threat to human health and ecosystems. Accurate differentiation and trace detection of fluoroquinolone antibiotics (FQs) in foods are imperative. Fluorine–doped carbon quantum dots chelated with Al³⁺ (FCQDs–Al³⁺) was prepared to design a ratiometric fluorescence sensor for the detection of ciprofloxacin (CIP) in milk. The fluorescence of the FCQDs at 360 nm was quenched by CIP and enhanced at 430 nm by CIP chelation with Al³⁺. The limit of detection of the sensor was 4.20 nM, and the spiked recovery rates in milk samples were 96.32 %–100.07 %. Machine learning methods confirmed quenching mechanism caused by the FF interaction and established identification models of six FQs. Therefore, fluoroquinolone antibiotics detection can be conducted qualitatively first and then quantitatively. The developed sensor had excellent quantitative results for the other five FQs. The method demonstrated significant potential in the identification and detection of FQs.