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

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 compre-hensive 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. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

This paper provides a critical and comprehensive review of the major benefits and potential pitfalls of current deep learning techniques used for spectral data modeling. Although it focuses on near-infrared (NIR) spectral data in chemometric tasks, many of the findings can be applied to other spectral techniques. Finally, empirical guidelines on the best practices for using deep learning for the modeling of spectral data are provided.