A MOF-on-MOF heterostructure ratiometric/colorimetric dual-mode fluorescence sensor based on support vector machine for detecting tetracyclines in animal-derived foods.

The misuse of tetracyclines in livestock production poses significant health risks. Thus, establishing convenient detection methods to replace complex laboratory tests for food safety is crucial. In this study, a heterostructure Zn-BTC/IRMOF-3 (denoted as ZBI) asynchronous response fluorescence sensor was developed for the qualitative and quantitative detection of tetracyclines in foods. The ZBI solution exhibited blue fluorescence under UV excitation; upon the introduction of tetracyclines, ZBI selectively recognized the tetracycline molecules through electron transfer, π-π stacking, and chelation, resulting in blue fluorescence quenching and green fluorescence enhancement. The ZBI sensor for tetracycline detection achieved recovery rates ranging from 93.91 to 111.91% in food samples, with a detection limit of as low as 0.086 µmol/L. Lastly, a portable sensing device using support vector classifier was constructed for detecting tetracyclines in real-life scenarios. Our findings introduce a new approach for fabricating fluorescence sensors and offer a novel method for detecting tetracyclines.

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

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.