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

() 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 gene ( T) from was initially amplified through PCR and subsequently captured via the nanogap effect of I and Mg-mediated bimetallic gold and silver nanoflowers (Au/Ag FL@I-Mg). These nanogaps generate hotspots for the direct signal amplification of enclosed 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 . 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.