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Title: Surface-enhanced Raman scattering (SERS) taster: a machine-learning-driven multireceptor platform for multiplex profiling of wine flavors
Authors: Leong, Yong Xiang
Lee, Yih Hong
Koh, Charlynn Sher Lin
Phan-Quang, Gia Chuong
Han, Xuemei
Phang, In Yee
Ling, Xing Yi
Keywords: Science::Chemistry
Issue Date: 2021
Source: Leong, Y. X., Lee, Y. H., Koh, C. S. L., Phan-Quang, G. C., Han, X., Phang, I. Y. & Ling, X. Y. (2021). Surface-enhanced Raman scattering (SERS) taster: a machine-learning-driven multireceptor platform for multiplex profiling of wine flavors. Nano Letters, 21(6), 2642-2649.
Project: A20E5c0082
Journal: Nano Letters
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.
ISSN: 1530-6984
DOI: 10.1021/acs.nanolett.1c00416
Schools: School of Physical and Mathematical Sciences 
Rights: © 2021 American Chemical Society. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

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