Nanogap-Assisted SERS/PCR Biosensor Coupled Machine Learning for the Direct Sensing of Staphylococcus aureus in Food

Area of Science:

• Food Safety
• Biosensor Technology
• Molecular Diagnostics

Background:

• Staphylococcus aureus (S. aureus) poses a significant risk to food safety.
• Accurate and rapid detection of S. aureus in food products like milk is crucial.

Purpose of the Study:

• To develop a direct and specific biosensing method for S. aureus in milk.
• To integrate surface-enhanced Raman scattering/polymerase chain reaction (SERS/PCR) with machine learning for enhanced detection.

Main Methods:

• Amplification of the S. aureus nuc gene using PCR.
• Capture and signal amplification of the nuc gene via nanogap effects using bimetallic gold and silver nanoflowers (Au/Ag FL@I-Mg2+).
• Analysis of SERS signals using machine learning algorithms, specifically bootstrapping soft shrinkage-partial least-squares.

Main Results:

• The developed nanogap-assisted SERS/PCR biosensor enabled direct and specific sensing of S. aureus.
• The machine learning model achieved high performance with a root mean-square error of prediction of 0.437 and a prediction set correlation coefficient of 0.967.
• A novel label-free strategy for S. aureus detection was successfully demonstrated.

Conclusions:

• The study presents an effective label-free strategy for the specific detection of S. aureus in milk.
• The biosensor platform shows potential for adaptation to detect other foodborne pathogenic bacteria by modifying specific primers.