An asynchronous response fluorescence sensor combines machine learning theory to qualitatively and quantitatively detect tetracyclines

Abstract

Excess use of tetracyclines poses significant health risks arising from animal-derived foods, meaning simple and sensitive methods to detect tetracyclines would be beneficial given current laboratory methods are complex and expensive. Herein, we describe an asynchronous response fluorescence sensor constructed based on Zn-based metal-organic framework and Ru(bpy)₃²⁺ (denoted as Ru@Zn-BTEC) for the qualitative and quantitative detection of tetracyclines in foods. Under excitation at 365 nm, the sensor emitted red fluorescence at 609 nm. When tetracyclines were present, these molecules aggregated in the Ru@Zn-BTEC framework, causing green fluorescence emission at 528 nm. The developed sensing system accurately distinguished the different categories of tetracyclines with a classifier accuracy of 94 %. The Ru@Zn-BTEC sensor demonstrated a detection limit of 0.012 μM and satisfactory recovery (87.81 %–113.84 %) for tetracyclines in food samples. This work provides a pathway for constructing asynchronous response fluorescence sensors for food analysis.