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

Abstract

Compatibility in a range of media is vitally important for surfaceenhanced 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 Mateŕn 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.

Biomimetic Transparent Nanoplasmonic Meshes by Reverse‐Nanoimprinting for Bio‐Interfaced Spatiotemporal Multimodal SERS Bioanalysis

“…The protonation/deprotonation of the heterocyclic nitrogen in 4‐Mpy as a function of pH can be detected by SERS. [ 37,38 ] The SERS spectra of 4‐Mpy in PBS at different pH values indicate that the intensity ratio between the ring breathing vibration mode at 998 cm –1 and the C–S band at 1085 cm –1 gradually decreased as the pH value of PBS increased (Figure 2H). [ 37 ] The peak ratio (I 1085 /I 998 ) was plotted as a function of bulk pH to generate a working curve for SERS‐enabled pH detection (Figure 2I).…”

Cost Management and Forecasting Method for the Whole Life Cycle of Water Conservancy Projects Based on Multiple Regression Analysis

“…Multiple regression analysis has a wider range of applications. Since linear regression analysis is relatively simple and common, the following first introduces multiple linear regression [25,26]. On the basis of linear analysis, dummy variable regression and a class of curve regression models that can be transformed into linear regression are gradually introduced.…”

Discrimination of Genetic Biomarkers of Disease through Machine-Learning-Based Hypothesis Testing of Direct SERS Spectra of DNA and RNA

“…We structured the SERS mutation detection task as a functional hypothesis test (HT) that is rooted in Gaussian process (GP) regression, allowing us to acknowledge the inherent functional nature of SERS data. While there have been recent studies that attempted to model SERS data using GPs, − developing GP-based HTs is an emerging area of research in ML, − and our work proposes a rigorous GP-based HT, referred to hereinafter as GP-HT, that is particularly tailored for the nuances and challenges of SERS data. By virtue of its flexible and probabilistic nature, our extensive experiments show that GP-HT has an exceptional discriminatory power in detecting mutations, measured by various performance metrics, including accuracy, sensitivity, and specificity, relative to a wide range of data-driven and ML-based approaches that do not address the functional nature of SERS data.…”