Surface-Enhanced Raman Scattering (SERS) Taster: A Machine-Learning-Driven Multireceptor Platform for Multiplex Profiling of Wine Flavors

Abstract

Integrating machine learning with surface-enhanced Raman scattering (SERS) accelerates the development of practical sensing devices. Such integration, in combination with direct detection or indirect analyte capturing strategies, is key to achieving high predictive accuracies even in complex matrices. However, in-depth understanding of spectral variations arising from specific chemical interactions is essential to prevent model overfit. Herein, we design a machine-learning-driven “SERS taster” to simultaneously harness useful vibrational information from multiple receptors for enhanced multiplex profiling of five wine flavor molecules at parts-per-million levels. Our receptors employ numerous noncovalent interactions to capture chemical functionalities within flavor molecules. By strategically combining all receptor–flavor SERS spectra, we construct comprehensive “SERS superprofiles” for predictive analytics using chemometrics. We elucidate crucial molecular-level interactions in flavor identification and further demonstrate the differentiation of primary, secondary, and tertiary alcohol functionalities. Our SERS taster also achieves perfect accuracies in multiplex flavor quantification in an artificial wine matrix.

Towards practical and sustainable SERS: a review of recent developments in the construction of multifunctional enhancing substrates

“…[164][165][166] For example, Ling, et al designed a "SERS taster" for profiling wine flavours by harnessing vibrational information of the intermolecular interactions between flavour molecules and SAMs of organic chemical receptor molecules functionalized on the surface of plasmonically active Ag nanocube films, as illustrated in Figure 11A. 167 Figure 11B shows the four different types of chemical receptors used for the analysis. Apart from the bare Ag nanoparticle receptor which provided SERS information by interacting directly with the flavour molecule, the other receptors were small molecules, which identified flavour molecules through intermolecular interactions that generated spectral changes.…”

Artificial intelligence: A powerful paradigm for scientific research

“…Principal-component analysis is employed for the discrimination of alcohols with varying degrees of substitution, and supported with vector machine discriminant analysis, is used to quantitatively classify all flavors with 100% accuracy. 172 Overall, AI techniques provide the first glimmer of hope for a universal method for spectral data analysis, which is fast, accurate, objective and definitive and with attractive advantages in a wide range of applications.…”

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

“…A broadly applied strategy involves the use of self-assembled monolayers (SAM) to promote the binding of analytes with distinct affinities to SERS sensors and to minimize nonspecific fouling. 173 − 175 SAM-functionalized substrates lead to specific physicochemical interactions between plasmonic substrates and different sample constituents. The nature of the selected SAM can be tailored to either improve specificity, the SAM may enhance binding of a small subset of molecules or even larger entities, such as exosomes, 174 , 176 or to increase multiplexing.…”