Photonic Data Science

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.