Rapid Identification and Quantification of Aqueous Antibiotics over a Machine Learning-Integrated Raman Sensor Array

up to 0.99 with markedly reduced prediction errors. The method is validated in river water, lake water, and municipal wastewater secondary effluent, yielding recoveries of 75.5-116% (median 97.5%), with 23/27 measurements within 80-110%, and an overall turnaround of ∼30 min from sample preparation to readout. Through coupling the dual-mode responsive Raman sensor array with data-driven modeling, this work provides a paradigm of rapid and multianalyte SERS analysis of structurally similar pollutants for on-site environmental monitoring.