Abstract：
Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.
摘要：
表面增强拉曼光谱(SERS),作为一种指纹识别和灵敏的分析技术，在包括生物医学在内的广泛领域具有很高的应用价值，环境保护,其他食品安全。在对永远敏感的无尽追求中，健壮,以及全面的传感和成像，进步在SERS的整个管道中不断涌现，从SERS底物和报告分子的设计，综合路线规划，仪器改进，数据预处理和分析方法。人工智能(AI)它是为了模仿并最终超越人类行为而创造的，在学习高级表示和识别具有出色自动性的复杂模式方面表现出了自己的能力。因此,面对相互交织的影响因素和爆炸性数据规模，人工智能在SERS的上述所有方面都得到了越来越多的利用，在加速系统优化和加深对基础物理和光谱数据的理解方面表现出精英效率，远远超越了人类的劳动和传统的计算。在这次审查中,通过人工智能的整合，总结了SERS的最新进展，并提供了对挑战和观点的新见解，旨在更好地将SERS推向快速通道。
