Sensitive and visual detection of sulfonamides using γ-CD-MOF@Eu-MOF fluorescence sensor assisted by smartphone and machine learning algorithm

γ-CD-MOF@Eu-MOF flower-like probe synthesized for sensitive sulfonamides detection. • Paper-based sensor enables portable, visual on-site testing via smartphone imaging. • Machine learning algorithm boosts detection speed and reliability for quantitation. • The detection limit of the γ-CD-MOF@Eu-MOF for SAs is as low as 25.5 nmol/L. The protection of human health and the guarantee of food safety necessitate rapid and precise identification of sulfonamides (SAs) residues in food. Herein, we propose a fluorescence sensor based on γ-CD-MOF@Eu-MOF, which is used for quantitative and visual detection of SAs. The γ-CD-MOF@Eu-MOF exhibits high selectivity toward SAs, with detection limits for sulfamerazine (SMR), sulfamethazine (SM 2 ), and sulfamethoxydiazine (SMD) as low as 25.5 nmol/L, 36.7 nmol/L, and 39.8 nmol/L, respectively. The fluorescent probes were immobilized on paper substrates to construct a paper-based sensor, which was combined with a smartphone-assisted machine learning algorithm to enable high-throughput, intelligent, and rapid detection of SAs. After optimizing the constructed prediction model, the coefficient of determination can reach 0.99, indicating the accuracy and applicability of the model. Overall, this sensor exhibits excellent stability, sensitivity, and specificity, providing a practical approach for the intelligent, rapid, and high-throughput on-site visual detection of SAs. Download: Download high-res image (163KB) Download: Download full-size image