Machine Learning-Assisted SERS Platform for Rapid and Quantitative Discrimination of Shiga Toxin-Producing E. coli Serotypes

TL;DR

A new SERS-based system with machine learning can quickly and accurately identify different types of Shiga toxin-producing E. coli, which is important for food safety and health.

Contribution

The novel integration of functionalized silver nanorod arrays with machine learning for rapid and quantitative E. coli serotype discrimination.

Findings

The SERS platform achieved 100% classification accuracy for single-concentration models of E. coli serotypes.

The system demonstrated an overall accuracy of 98.41% when analyzing all concentrations and serotypes together.

The platform enables multiplexed, high-throughput bacterial diagnostics using a portable Raman spectrometer.

Abstract

Rapid, sensitive, and specific detection of pathogenic Escherichia coli serotypes is crucial for food safety and public health. Here, we present a surface-enhanced Raman scattering (SERS) platform utilizing highly ordered silver nanorod (AgNR) arrays functionalized with vancomycin for efficient and selective bacterial capture. The system enables multiplexed, high-throughput analysis using a portable Raman spectrometer, achieving direct molecular fingerprinting of seven clinically relevant E. coli serotypes. Systematic optimization of AgNR length and vancomycin coating maximized SERS enhancement and capture efficiency. Advanced data analysis with linear discriminant analysis (LDA) provided robust discrimination among all serotypes and concentrations, achieving up to 100% classification accuracy in single-concentration models and an overall accuracy of 98.41% when all concentrations and serotypes were evaluated jointly. This integrated SERS approach demonstrates significant promise for rapid, on-site bacterial diagnostics and quantitative pathogen monitoring, paving the way for practical applications in food safety and clinical microbiology.

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