SERS and supervised learning for proof-of-concept rotavirus detection in wastewater matrices

Area of Science:

• Nanotechnology and Materials Science
• Biomedical Engineering
• Environmental Science and Public Health

Background:

• Rotavirus is a major cause of gastroenteritis globally.
• Wastewater surveillance is crucial for early Rotavirus outbreak detection.
• Existing detection methods often require time-consuming amplification.

Purpose of the Study:

• Develop a rapid, sensitive, and cost-effective biosensor for Rotavirus detection in wastewater.
• Utilize surface-enhanced Raman scattering (SERS) and machine learning for analysis.
• Overcome limitations of traditional molecular amplification techniques.

Main Methods:

• Fabrication of large-area plasmonic substrates using gold nanoparticle self-assembly.
• Functionalization of substrates with anti-Rotavirus antibodies.
• Detection via SERS and data analysis using a support vector machine (SVM) classifier.

Main Results:

• Demonstrated high uniformity, reproducibility, and stability of SERS substrates.
• Achieved a limit of detection of 3.9 TCID50/mL for Rotavirus in controlled samples.
• Obtained 0.89 average accuracy and 0.79 Cohen's κ for Rotavirus detection in real wastewater.

Conclusions:

• The SERS-biosensor platform offers sensitive and selective Rotavirus detection.
• The integrated SERS-machine learning approach is feasible for wastewater screening.
• This technology supports wastewater-based epidemiology for public health monitoring.