Machine Learning-Assisted Chemical Tongues Based on Dual-channel Inclusion Complexes for Rapid Identification of Nonsteroidal Anti-inflammatory Drugs in Food

Abstract

The improper application of nonsteroidal anti-inflammatory drugs (NSAIDs) presents significant health hazards via vector food contamination. A critical limitation of these traditional existing approaches is their inability to concurrently discern and distinguish among diverse NSAIDs, presenting a notable gap in the analytical capabilities within this domain. Herein, a creative dual-channel fluorescence sensor array was developed for the rapid discrimination and determination of NSAIDs, utilizing complexes of cucurbit[8]uril (CB[8]) with three distinct modified poly(ethylenimines) (PEIs) to address this challenge. The array successfully differentiated and identified 19 NSAIDs with 97% accuracy at a concentration of 1 mM. In addition, it also achieved analyses of individual NSAIDs across a range of concentrations, NSAID mixtures, and impurities of aspirin using statistical analysis methods. More importantly, the approach effectively detected NSAIDs in complex matrices, such as milk and urine, demonstrating its potential for real-world applications.