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

Staphylococcus aureus (S. aureus) is the primary risk factor in food safety. Herein, a nanogap-assisted surface-enhanced Raman scattering/polymerase chain reaction (SERS/PCR) biosensor coupled with a machine-learning tool was developed for the direct and specific sensing of S. aureus in milk. The specific nuc gene (nuc T) from S. aureus was initially amplified through PCR and subsequently captured via the nanogap effect of I^- and Mg²⁺-mediated bimetallic gold and silver nanoflowers (Au/Ag FL@I^--Mg²⁺). These nanogaps generate hotspots for the direct signal amplification of enclosed nuc T. Subsequently, machine-learning tools were used to comparatively analyze the collected SERS signals. The bootstrapping soft shrinkage-partial least-squares method exhibited superior performance (root mean-square error of prediction: 0.437, prediction set correlation coefficient: 0.967). This study demonstrated a novel label-free strategy for specifically detecting S. aureus. The strategy could be advanced to serve as a platform for application to other types of foodborne pathogenic bacteria by engineering a suitable specific primer.