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

Puneet Mishra, Dário Passos, Federico Marini, Junli Xu, Jose M. Amigo, Aoife A. Gowen, Jeroen J. Jansen, Alessandra Biancolillo, Jean Michel Roger, Douglas N. Rutledge, Alison Nordon

Research output: Contribution to journalReview articlepeer-review

16 Citations (Scopus)
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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.
Original languageEnglish
Article number116804
Pages (from-to)1-8
Number of pages8
JournalTrAC - Trends in Analytical Chemistry
Early online dateOct 2022
Publication statusPublished - Dec 2022


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