Surface-enhanced Raman scattering (SERS) is a powerful technique for sensitive label-free analysis of chemical and biological samples. While much recent work has established sophisticated automation routines using machine learning and related artificial intelligence methods, these efforts have largely focused on downstream processing (e.g., classification tasks) of previously collected data. While fully automated analysis pipelines are desirable, current progress is limited by cumbersome and manually intensive sample preparation and data collection steps. Specifically, a typical lab-scale SERS experiment requires the user to evaluate the quality and reliability of the measurement (i.e., the spectra) as the data are being collected. This need for expert user-intuition is a major bottleneck that limits applicability of SERS-based diagnostics for point-of-care clinical applications, where trained spectroscopists are likely unavailable. While application-agnostic numerical approaches (e.g., signal-to-noise thresholding) are useful, there is an urgent need to develop algorithms that leverage expert user intuition and domain knowledge to simplify and accelerate data collection steps. To address this challenge, in this work, we introduce a machine learning-assisted method at the acquisition stage. We tested six common algorithms to measure best performance in the context of spectral quality judgment. For adoption into future automation platforms, we developed an open-source python package tailored for rapid expert user annotation to train machine learning algorithms. We expect that this new approach to use machine learning to assist in data acquisition can serve as a useful building block for point-of-care SERS diagnostic platforms.

表面增强拉曼散射 (SERS) 是一种强大的技术，用于对化学和生物样品进行灵敏的无标记分析。虽然最近的许多工作已经使用机器学习和相关人工智能方法建立了复杂的自动化例程，但这些努力主要集中在先前收集的数据的下游处理（例如分类任务）。虽然全自动分析流程是可取的，但目前的进展受到繁琐且手动密集的样品制备和数据收集步骤的限制。具体来说，典型的实验室规模的 SERS 实验要求用户在收集数据时评估测量（即光谱）的质量和可靠性。这种对专家用户直觉的需求是一个主要瓶颈，限制了基于 SERS 的诊断在护理点临床应用中的适用性，而在这些应用中，可能没有经过培训的光谱学家。虽然与应用无关的数值方法（例如，信噪比阈值）很有用，但迫切需要开发利用专家用户直觉和领域知识来简化和加速数据收集步骤的算法。为了应对这一挑战，在这项工作中，我们在采集阶段引入了机器学习辅助方法。我们测试了六种常见算法，以衡量光谱质量判断中的最佳性能。为了在未来的自动化平台中采用，我们开发了一个开源 python 包，专为快速专家用户注释而定制，以训练机器学习算法。我们期望这种使用机器学习辅助数据采集的新方法可以成为护理点 SERS 诊断平台的有用构建模块。