Deep learning for near-infrared spectral data modelling : hypes and benefits

Tools

Mishra, Puneet and Passos, Dário and Marini, Federico and Xu, Junli and Amigo, José M. and Gowen, Aoife A. and Jansen, Jeroen J. and Biancolillo, Alessandra and Roger, Jean Michel and Rutledge, Douglas N. and Nordon, Alison (2022) Deep learning for near-infrared spectral data modelling : hypes and benefits. Trends in Analytical Chemistry, 157. 116804. ISSN 0165-9936 (https://doi.org/10.1016/j.trac.2022.116804)

[thumbnail of Mishra-etal-TAC-2022-Deep-learning-for-near-infrared-spectral-data-modelling-hypes-benefits]
Preview
 Text. Filename: Mishra_etal_TAC_2022_Deep_learning_for_near_infrared_spectral_data_modelling_hypes_benefits.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (1MB)| Preview

Abstract

Deep learning (DL) is emerging as a new tool to model spectral data acquired in analytical experiments. Although applications are flourishing, there is also much interest currently observed in the scientific community on the use of DL for spectral data modelling. This paper provides a critical and comprehensive review of the major benefits, and potential pitfalls, of current DL tecnhiques used for spectral data modelling. Although this work focuses on DL for the modelling of near-infrared (NIR) spectral data in chemometric tasks, many of the findings can be expanded to cover other spectral techniques. Finally, empirical guidelines on the best practice for the use of DL for the modelling of spectral data are provided.

ORCID iDs

Mishra, Puneet, Passos, Dário, Marini, Federico, Xu, Junli, Amigo, José M., Gowen, Aoife A., Jansen, Jeroen J., Biancolillo, Alessandra, Roger, Jean Michel, Rutledge, Douglas N. and Nordon, Alison ORCID logoORCID: https://orcid.org/0000-0001-6553-8993;
CORE (COnnecting REpositories)