拉曼散射光子超光谱成像：拉曼分析的新范例
Hyperspectral imaging with Raman scattered photons: A new paradigm in Raman analysis
摘要：
表面增强拉曼光谱是分析科学和技术的一项重要技术，其中分析物的放大拉曼光谱用于化学指纹。在这里，我们展示了一个工程分层基板中的等离子体激元活性区域被限制在微米级，二维六边形图案。因此，均匀涂覆基底的任何分析物的拉曼信号增强与2D图案具有高度配准。这种空间分离的增强允许仅使用来自分析物的拉曼散射光子对2D图案进行光学成像。虽然图案亮度和对比度决定分析物识别和检测灵敏度，但光谱选择性对比度允许调谐特异性。通过用罗丹明和荧光素采集拉曼图像证明了该技术的概念性证明，并最终应用于检测40 mM浓度的葡萄糖。使用基于机器学习的自动模式识别方案实现的阳性检测的大面积成像和空间均匀性的固有要求增加了分析物检测的统计置信度。这种同时进行的多点信号检测牺牲了连续的光谱信息，代价是速度、再现性和人为误差，这是通过自动检测这里提出的高光谱成像技术实现的。
英文摘要：
Surface enhanced Raman spectroscopy, is a technique of fundamental importance to analytical science and technology where the amplified Raman spectrum of analytes is used for chemical fingerprinting. Here, we showcase an engineered hierarchical substrate in which the plasmonically active regions are restricted to a micron scale, 2D hexagonal pattern. The Raman signal enhancement of any analyte uniformly coating the substrate is consequently bears a high registry with the 2D pattern. This spatially segregated enhancement allows optical imaging of the 2D pattern solely using the Raman scattered photons from the analyte. While pattern brightness and contrast determine analyte identification and detection sensitivity, the spectrally selective contrast allows for tuning specificity. Conceptual proof of the technique is demonstrated via the acquisition of Raman images with rhodamine and fluorescein and finally applied to detect glucose in 40 mM concentration. The large area imaging and the inherent requirement of spatial uniformity for positive detection implemented using a machine learning based automated pattern recognition protocol increases the statistical confidence of analyte detection. This simultaneously multisite signal detection sacrifices continuous spectral information at the cost of speed, reproducibility and human error via automation of detection of the hyperspectral imaging technique presented here.
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