MATLAB-assisted visual ratiometric fluorescence sensing of tetracycline based on antenna effect

Tetracycline (TC) is a widely used antibiotic that accumulates in animal foods, posing a threat to human health and even causing environmental pollution. This research aimed to establish a rapid method for the detection of TC. To this end, sodium alginate-protected gold nanoclusters (SA-AuNCs) were synthesized, which combined with europium ions (Eu3+) to construct a ratiometric-sensing platform for the detection of TC. When added TC, Eu3+ interacted with TC could result in enhanced fluorescence at 620 nm, which was used as a response signal, while the fluorescence intensity of SA-AuNCs remained unchanged, acting as a reference signal. Accordingly, a linear relationship was established in a wide range of TC (0-60 mu M) and the detection limit (LOD) was 22.07 nM. The recoveries of the spiked samples ranged from 94.71 % to 109.73 %, implying that the as-proposed method could be successfully applied to detect TC in real samples. Furthermore, color information was extracted from the fluorescence images taken with a smartphone and analyzed by MATLAB, in which a visual mini program could be designed for calculating the channel values of red (R), green (G), and blue (B) of the generated fluorescence images. Utilizing the image processing functions and data processing capabilities of MATLAB, the obtained R/G values were selected for the semi-quantitative detection of TC in real samples without any time-consuming pretreatment and expensive instruments, demonstrating the great potential prospects of integrating MATLAB algorithm with SA-AuNCs-based ratiometric fluorescence probe into one ideal system for the development of rapid food assay.

Automated summary

This study established a rapid method for the detection of tetracycline (TC) by utilizing sodium alginate-protected gold nanoclusters (SA-AuNCs) and europium ions (Eu3+). The proposed method showed a linear relationship over a wide range of TC concentrations, with a low detection limit and high recovery rates. Additionally, color information extracted from fluorescence images taken with a smartphone was analyzed using MATLAB, enabling semi-quantitative detection of TC in real samples without the need for time-consuming pretreatment or expensive instruments.