Handheld raman spectrometer for rapid microbial detection using surface enhanced raman scattering

Overview

This solution offers a portable, handheld Raman spectrometer platform designed for the rapid detection and identification of microbial contaminants in aqueous solutions. By combining silver nanorod (AgNR) based surface enhanced Raman scattering (SERS) substrates with machine learning algorithms, the system can screen, differentiate, and identify a wide range of bacteria and fungi in minutes rather than hours. The technology addresses critical needs in food safety, clinical diagnostics, water quality monitoring, and agricultural testing, where conventional culture-based methods are slow, labor-intensive, and require laboratory infrastructure. The proposed platform aims to deliver a limit of detection below 10 CFU/ml at a cost of less than $10 per test, making large-scale, on-site microbial screening practical and affordable.

Technical specifications

Key features:

• Silver nanorod SERS substrates amplify Raman signals from microbial cells, enabling detection at extremely low concentrations
• Machine learning algorithms classify and differentiate microbial species, serotypes, and strains from SERS spectral data
• Handheld, portable Raman spectrometer allows field deployment without the need for a centralized laboratory
• Two-step filtration process concentrates target pathogens from complex sample matrices such as food rinsates
• Broad microbial coverage demonstrated across 27 bacterial isolates spanning 12 species, including pathogenic and drug-resistant strains
• Gram classification capability distinguishes Gram-positive from Gram-negative bacteria with high sensitivity, specificity, and 100% accuracy on test samples

Performance targets:

• Limit of detection: less than 10 CFU/ml (current demonstrated performance: less than 100 CFU/ml)
• Detection time: significantly less than 2 hours (current demonstrated performance: less than 4 hours)
• Per-test cost: less than $10

Technology readiness level

The technology is currently at a mid-to-late stage of development. Proof-of-concept studies have been completed using AgNR-SERS substrates with 27 different bacterial isolates, demonstrating species-level differentiation, serotype and strain-level resolution, and reliable Gram classification. A portable handheld Raman system has been validated on foodborne pathogens in mung bean sprouts, achieving detection at 100 CFU/ml in under 4 hours. Future validation efforts over the next year will focus on expanding the spectral library to include additional bacteria and fungi, refining machine learning models to push sensitivity toward 10 CFU/ml, and improving substrate consistency and reliability. Collaborative work with the Center for Food Safety at the University of Georgia will provide access to diverse microbial samples for training and validation. The platform is ready for sponsored research partnerships aimed at field validation, co-development of application-specific workflows, and pilot deployments in food processing, healthcare, and environmental monitoring settings.