Enhancement of the Au/ZnO-NA Plasmonic SERS Signal Using Principal Component Analysis as a Machine Learning Approach

In this work, we modeled a novel approach to enhance surface-enhanced Raman scattering (SERS) signals using principal component analysis (PCA) as a machine learning approach. Zinc oxide nanoarrays (ZnO-NAs) were synthesized using a hydrothermal method followed by zinc oxide nucleation on ITO glass substrates via an oxidation furnace at 500 °C. The surface morphology was improved by short rapid thermal annealing (S-RTA) after deposition of a gold layer via a thermal evaporator to avoid chemical contamination of the sensing surface, which is a suitable plasmonic platform for the generation of “hot spots” for SERS enhancement with fewer defects. The proposed Au/ZnO-NA SERS sensor exhibited an enhancement factor (EF) of 1.15 × 10
⁷
via the R6G Raman probe and excellent uniformity over the entire surface. The PCA algorithm was used to extract useful features and information from the SERS signal. The algorithm was implemented with MATLAB software (R2019a) by the multivariable analytical tool to find an enhanced signal (~3 times higher) with high uniformity, which has great potential and is applicable to a wide range of probe molecules suitable in medical, safety, and environmental applications.