Abstract
Environmental pollution caused by ciprofloxacin is a major problem of global public health. A machine learning-assisted portable smartphone-based visualized molecularly imprinted electrochemiluminescence (MIECL) sensor was developed for the highly selective and sensitive detection of ciprofloxacin (CFX) in food. To boost the efficiency of electrochemiluminescence (ECL), oxygen vacancies (OVs) enrichment was introduced into the flower-like Tb@Lu2O3 nanoemitter. With the specific recognition reaction between MIP as capture probes and CFX as detection target, the ECL signal significantly decreased. According to, CFX analysis was determined by traditional ECL analyzer detector in the concentration range from 5 × 10−4 to 5 × 102 μmol L−1 with the detection limit (LOD) of 0.095 nmol L−1 (S/N = 3). Analysis of luminescence images using fast electrochemiluminescence judgment network (FEJ-Net) models, achieving portable and intelligent quick analysis of CFX. The proposed MIECL sensor was used for CFX analysis in real meat samples and satisfactory results, as well as efficient selectivity and good stability.
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