Work

RADAR: Raman Spectral Analysis Using Deep Learning for Artifact Removal (Advanced Optical Materials 25/2025)

Year: 2025

Type: article

Abstract: Raman Spectroscopy In article 10.1002/adom.202500736, Ion Petre and co-workers introduce RADAR, two lightweight deep learning models that simultaneously denoise and correct Raman spectra, reducing data acquisition time by up to 90% while preserving signal integrity. By streamlining artifact removal, RADAR enhances the speed, accuracy, and usability of Raman spectroscopy across diverse applications in materials science, biomedical research, and beyond.

Source: Advanced Optical Materials

DOI: 10.1002/adom.70254

Authors: Joel Sjöberg, Nicoleta Siminea, Andrei Păun, Adrian Lita, Mioara Larion, Ion Petre

Institutions: University of Turku, University of Bucharest, National Institute of Research and Development for Biological Sciences, Romanian Academy, National Institutes of Health, National Cancer Institute

Language: English

Topic: Spectroscopy Techniques in Biomedical and Chemical Research

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: bronze

OpenAlex ID: W4413953597

FWCI:

Cites:

Cited by:

Related to: 10