Portable, intelligent MIECL sensing platform for ciprofloxacin detection using a fast convolutional neural networks-assisted Tb@Lu2O3 nanoemitter

A deep learning-assisted smartphone visualized molecularly imprinted electrochemiluminescence (MIECL) sensing platform for ciprofloxacin analysis based on self-developed portable devices and oxygen vacancy structure. [Display omitted] •Tb@Lu2O3 as a cathode luminophor is applied in ECL system for the first time.•Oxygen vacancy engineering in Tb@Lu2O3 emitter for ECL sensing of CFX.•A self-enhanced MIECL complex is prepared by linked MIP with ECL.•A deep learning-assisted visualized-based portable MIECL sensor has been successfully established.•ECL image analysis based on modified residual neural network (ResNet) model. 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.