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Deep Learning for Reconstructing Low-Quality FTIR and Raman Spectra—A Case Study in Microplastic Analyses
[Image: see text] Herein we report on a deep-learning method for the removal of instrumental noise and unwanted spectral artifacts in Fourier transform infrared (FTIR) or Raman spectra, especially in automated applications in which a large number of spectra have to be acquired within limited time. A...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
American Chemical Society
2021
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8674871/ https://www.ncbi.nlm.nih.gov/pubmed/34807556 http://dx.doi.org/10.1021/acs.analchem.1c02618 |