Developing a highly sensitive and accurate method to discriminate between amino acids and peptides is vital for establishing future healthcare testing technologies, such as liquid biopsy. This study proposes a highly sensitive technique based on surface-enhanced Raman scattering (SERS), which combines chemically linking an analyte with gold nanoparticles and aggregating them to produce hotspots. Furthermore, by combining rapid SERS imaging with slit-scanning Raman microscopy and deep learning based on a convolutional neural network, 20 proteinogenic amino acids were successfully detected and distinguished with accuracies exceeding 95%. Also, out of 39 types of dipeptides that have Phe at either the amino terminal or the carboxyl terminal, 19 types were identified with high accuracy. Even for dipeptides with lower identification accuracy, it was confirmed that they were recognized as one of the dipeptides with high structural similarity, such as cyclic structures and branched amino acids. Moreover, pathophysiologically relevant sequence differences in β-amyloid peptides were accurately discriminated with a sensitivity of approximately 975 zeptomoles.

中文翻译：

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快速表面增强拉曼散射成像和深度学习，用于氨基酸和肽的高灵敏度区分


开发一种高灵敏度和准确的方法来区分氨基酸和肽，对于建立未来的医疗保健检测技术（如液体活检）至关重要。本研究提出了一种基于表面增强拉曼散射 （SERS） 的高灵敏度技术，该技术将分析物与金纳米颗粒的化学连接相结合，并将它们聚集以产生热点。此外，通过将快速 SERS 成像与狭缝扫描拉曼显微镜和基于卷积神经网络的深度学习相结合，成功检测和区分了 20 个蛋白原性氨基酸，准确率超过 95%。此外，在氨基末端或羧基末端具有 Phe 的 39 种二肽中，鉴定出 19 种类型的准确度很高。即使对于鉴定准确率较低的二肽，也证实了它们被公认为具有高度结构相似性的二肽之一，例如环状结构和支链氨基酸。此外，准确区分了 β-淀粉样蛋白肽的病理生理学相关序列差异，灵敏度约为 975 zeptomoles。