Division of Chemistry and Biological Chemistry, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore.
Nano Lett. 2021 Mar 24;21(6):2642-2649. doi: 10.1021/acs.nanolett.1c00416. Epub 2021 Mar 12.
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.
将机器学习与表面增强拉曼散射 (SERS) 相结合，加速了实用传感设备的发展。这种集成，结合直接检测或间接分析物捕获策略，是实现高预测精度的关键，即使在复杂基质中也是如此。然而，深入了解特定化学相互作用引起的光谱变化对于防止模型过度拟合至关重要。在此，我们设计了一种机器学习驱动的“SERS 味觉仪”，可同时从多个受体中提取有用的振动信息，以实现百万分之几水平的五种葡萄酒风味分子的增强多路谱分析。我们的受体采用了许多非共价相互作用来捕获风味分子中的化学官能团。通过策略性地组合所有受体-风味 SERS 光谱，我们使用化学计量学构建了用于预测分析的综合“SERS 超级谱”。我们阐明了风味识别中的关键分子水平相互作用，并进一步证明了伯、仲和叔醇官能团的差异。我们的 SERS 味觉仪在人工葡萄酒基质中的多路风味定量中也达到了完美的精度。
