Applications of machine learning in spectroscopy

• Published in: Applied spectroscopy reviews Vol. 56; no. 8-10; pp. 733 - 763
• Main Authors: Meza Ramirez, Carlos A., Greenop, Michael, Ashton, Lorna, Rehman, Ihtesham ur
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 26.11.2021; Taylor & Francis Ltd
• Subjects: Artificial intelligence; chemometrics; Data science; Deep learning; Infrared and Raman spectroscopy; Machine learning; Spectrum analysis; Symbiosis
• ISSN: 0570-4928; 1520-569X
• DOI: 10.1080/05704928.2020.1859525

The way to analyze data in spectroscopy has changed substantially. At the same time, data science has evolved to the point where spectroscopy can find space to be housed, adapted and be functional. The integration of the two sciences has introduced a knowledge gap between data scientists who know about advanced machine learning techniques and spectroscopists who have a solid background in chemometrics. To reach a symbiosis, the knowledge gap requires bridging. This review article focuses on introducing data science subjects to non-specialist spectroscopists, or those unfamiliar with the subject. The article will explain concepts that are covered in machine learning, such as supervised learning, unsupervised learning, deep learning, and most importantly, the difference between machine learning and artificial intelligence. This article also includes examples of published spectroscopy research, in which some of the concepts explained here are applied. Machine learning together with spectroscopy can provide a useful, fast, and efficient tool to analyze samples of interest both for industrial and research purposes.