Surface-Enhanced Raman Scattering (SERS) Taster: A Machine-Learning-Driven Multireceptor Platform for Multiplex...

Area of Science:

• Analytical Chemistry
• Chemometrics
• Spectroscopy

Background:

• Integrating machine learning (ML) with surface-enhanced Raman scattering (SERS) accelerates practical sensor development.
• High predictive accuracy in complex matrices requires understanding spectral variations and preventing model overfit.
• Multiplex profiling of analytes demands strategies for capturing diverse chemical functionalities.

Purpose of the Study:

• To develop a machine-learning-driven SERS sensor for simultaneous multiplex profiling of wine flavor molecules.
• To enhance predictive accuracy by harnessing vibrational information from multiple receptors.
• To elucidate molecular interactions for improved flavor identification and quantification.

Main Methods:

• Designed a
• SERS taster
• utilizing multiple receptors with noncovalent interactions.
• Generated comprehensive
• SERS superprofiles
• by combining spectra from various receptor-flavor interactions.
• Employed chemometrics for predictive analytics and molecular-level interaction elucidation.

Main Results:

• Successfully achieved simultaneous multiplex profiling of five wine flavor molecules at parts-per-million levels.
• Demonstrated the differentiation of primary, secondary, and tertiary alcohol functionalities based on SERS spectra.
• Attained perfect accuracies in multiplex flavor quantification within an artificial wine matrix.

Conclusions:

• The ML-driven
• SERS taster
• offers a powerful approach for sensitive and selective analysis of complex mixtures.
• Understanding noncovalent interactions is crucial for optimizing SERS-based sensing.
• This technology shows significant potential for real-world applications in food and beverage analysis.