Nanostructured Au-Based Surface-Enhanced Raman Scattering Substrates and Multivariate Regression for pH Sensing

Compatibility in a range of media is vitally important for surface-enhanced Raman scattering (SERS)-enabled pH detection. We report universal pH detection in a range of media using top-down nanostructured gold SERS substrates and multivariate regression. SERS substrates with vertically stacked multiple nanogap hotspots functionalized with the sensing molecule 4-mercaptopyridine (4-Mpy) exhibited high spatial uniformity. Standard ratiometric pH detection enabled development of a Boltzmann equation-based calibration curve for phosphate-buffered saline. This calibration curve, however, could not be used to predict pH in other media such as carbonate buffer, apple juice, milk, and wastewater. To address SERS interferences that occur in these different media compositions, multivariate regression was successfully applied to pH prediction for all five media. A total of 19 spectral features in the 4-Mpy SERS spectra was extracted and used for model development. A nonparametric Gaussian process regression model with a 5/2 Matérn kernel function exhibited the greatest pH prediction accuracy with a root-mean-square error of 0.81 among other multivariate regression models. This model was generalizable and capable of determining pH within media that had not been used for model training.