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

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, indepth 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 function-alities 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.

Automated summary

Integration of machine learning with surface-enhanced Raman scattering accelerates the development of practical sensing devices for enhanced multiplex profiling of flavor molecules. By strategically combining receptor-flavor SERS spectra, comprehensive SERS superprofiles are constructed for predictive analytics, elucidating crucial molecular-level interactions in flavor identification.