The overuse and misuse of tetracycline (TCs) antibiotics, including tetracycline (TTC), oxytetracycline (OTC), doxycycline (DC), and chlortetracycline (CTC), pose a serious threat to human health. However, current rapid sensing platforms for tetracyclines can only quantify the total amount of TCs mixture, lacking real-time identification of individual components. To address this challenge, we integrated a deep learning strategy with fluorescence and colorimetry-based multi-mode logic gates in our self-designed smartphone-integrated toolbox for the real-time identification of natural TCs. Our ratiometric fluorescent probe (CD-Au NCs@ZIF-8) encapsulated carbon dots and Au NCs in ZIF-8 to prevent false negative or positive results. Additionally, our independently developed WeChat app enabled linear quantification of the four natural TCs using the fluorescence channels. The colorimetric channels were also utilized as outputs of logic gates to achieve real-time identification of the four individual natural tetracyclines. We anticipate this strategy could provide a new perspective for effective control of antibiotics.
Keywords: Deep learning; Identification; Logic gates; Smartphone; Tetracycline antibiotics.
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