Applications of machine learning in spectroscopy

Meza Ramirez, C.A. and Greenop, M. and Ashton, L. and Rehman, I.U. (2021) Applications of machine learning in spectroscopy. APPLIED SPECTROSCOPY REVIEWS, 56 (8-10). pp. 733-763. ISSN 0570-4928

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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. © 2020 Taylor & Francis Group, LLC.

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This is an Accepted Manuscript of an article published by Taylor & Francis in Applied Spectroscopy Reviews on 23/12/2020, available online:
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?? artificial intelligencechemometricsdata scienceinfrared and raman spectroscopymachine learningdata sciencedeep learningindustrial researchchemometricsknowledge gapsmachine learning techniquesresearch purposespectroscopy researchlearning systemsinstrumenta ??
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28 Oct 2021 12:15
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15 Jul 2024 21:19