Using Raman spectroscopy and machine learning for rapid, label-free bacterial detection in wastewater

Stanford scientists have developed a platform that combines Raman spectroscopy, nanomaterials, and machine learning to rapidly identify bacteria in wastewater without chemical labels. This integrated approach uses engineered surfaces and electric fields to concentrate bacteria for enhanced detection, enabling sensitive and accurate identification of multiple bacterial species in complex wastewater samples. The technology could transform wastewater-based epidemiology by providing rapid, early warnings of bacterial disease outbreaks in communities.

Wastewater-based epidemiology has emerged as a valuable tool for monitoring community health and providing early warnings of disease outbreaks. While this approach has been successful for viral diseases, the ability to rapidly identify bacterial pathogens in wastewater remains a critical challenge for public health surveillance. Current methods like PCR require species-specific reagents and are difficult to scale for multiple bacterial species, while traditional culturing can take weeks to generate results. This delay is particularly problematic during disease outbreaks when rapid screening of numerous samples is essential. Additionally, the complex nature of wastewater, containing various metals, ions, biomolecules, and microorganisms, makes it challenging to accurately detect target bacteria that represent only a small fraction of the sample. A rapid, label-free method for bacterial identification in wastewater could transform our ability to monitor bacterial infections at the population level and enable faster public health responses to emerging threats.

The Generalized Enrichment with Raman-Machine learning Spectroscopy (GERMS) platform combines advanced nanomaterials, electric field concentration, and machine learning to enable rapid bacterial identification in wastewater. The system achieves over 96% accuracy in bacterial classification within minutes rather than days, requires no chemical labels or reagents, and can be manufactured cost-effectively using standard semiconductor processes. The platform's combination of speed, accuracy, and scalability makes it particularly promising for wastewater-based epidemiology, enabling rapid detection of bacterial outbreaks and monitoring of antibiotic-resistant strains across various settings, from municipal systems to underdeveloped regions.

Stage of Development:
Proof of concept
Continued research – Pilot demonstration on real wastewater