Metabolic Fingerprint-Mediated Chemical Nose Strategy: Precise Identification of Foodborne Bacteria Based on a 4-MU Derivative Fluorescence Array.

Accurate identification of foodborne microorganisms is a key priority for food safety. Focused on the common principles of microbial metabolism, a metabolic-assisted chemical nose strategy was developed in this work. Consequently, the rapid, low-cost, and accurate identification of multiple bacteria can be performed independently of prior knowledge regarding specific bacterial strains. By utilizing commercially available 4-Methylumbelliferone (4-MU) derivatives, we constructed a multichannel fluorescence sensor array without the need for complex chemical modification. The differential fluorescence responses of various bacteria to these substrates were captured and translated into unique digital "metabolic fingerprints". Nine common foodborne microorganisms, including Escherichia coli O157:H7, Staphylococcus aureus, Salmonella typhimurium, Shigella dysenteriae, were successfully identified with 100% accuracy by integrating with machine learning algorithms such as linear discriminant analysis (LDA). In addition, further evaluation in real samples showed that the metabolism-assisted strategy exhibited good anti-interference and recognition ability based on the unique sensing mechanism, and the array could accurately distinguish the nine bacteria in milk samples after only a simple pretreatment. This study provides a general and efficient analytical platform for the rapid detection and classification of bacteria without relying on specific biological recognition elements, thereby holding considerable potential for application in food safety and related fields.