Vulchi Ravi Teja, Morgunov Volodymyr, Junjuri Rajendhar, Bocklitz Thomas
Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany.
Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Strasse 9, 07745 Jena, Germany.
Molecules. 2024 Oct 8;29(19):4748. doi: 10.3390/molecules29194748.
Raman spectroscopy, renowned for its unique ability to provide a molecular fingerprint, is an invaluable tool in industry and academic research. However, various constraints often hinder the measurement process, leading to artifacts and anomalies that can significantly affect spectral measurements. This review begins by thoroughly discussing the origins and impacts of these artifacts and anomalies stemming from instrumental, sampling, and sample-related factors. Following this, we present a comprehensive list and categorization of the existing correction procedures, including computational, experimental, and deep learning (DL) approaches. The review concludes by identifying the limitations of current procedures and discussing recent advancements and breakthroughs. This discussion highlights the potential of these advancements and provides a clear direction for future research to enhance correction procedures in Raman spectral analysis.
拉曼光谱以其提供分子指纹的独特能力而闻名，是工业和学术研究中一项极其宝贵的工具。然而，各种限制因素常常阻碍测量过程，导致伪像和异常情况，这些会显著影响光谱测量。本综述首先深入讨论这些源于仪器、采样和样品相关因素的伪像和异常情况的起源及影响。在此之后，我们给出了现有校正程序的全面列表和分类，包括计算方法、实验方法和深度学习（DL）方法。综述最后指出了当前程序的局限性，并讨论了近期的进展和突破。这一讨论突出了这些进展的潜力，并为未来研究增强拉曼光谱分析中的校正程序提供了明确的方向。
