TY - JOUR
T1 - Deep learning for near-infrared spectral data modelling
T2 - Hypes and benefits
AU - Mishra, Puneet
AU - Passos, Dário
AU - Marini, Federico
AU - Xu, Junli
AU - Amigo, Jose M.
AU - Gowen, Aoife A.
AU - Jansen, Jeroen J.
AU - Biancolillo, Alessandra
AU - Roger, Jean Michel
AU - Rutledge, Douglas N.
AU - Nordon, Alison
N1 - Funding Information:
Junli Xu and Aoife Gowen acknowledge funding from Science Foundation Ireland (SFI) under the investigators programme Proposal ID 15/IA/2984-HyperMicroMacro. Dário Passos acknowledges FCT - Fundação para a Ciência e a Tecnologia , Portugal, for funding CEOT project UIDB/00631/2020 CEOT BASE and UIDP/00631/2020 CEOT PROGRAMÁTICO.
Publisher Copyright:
© 2022 The Author(s)
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Chemometrics
KW - Near-infrared
KW - Neural networks
KW - NIR
KW - Spectroscopy
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U2 - 10.1016/j.trac.2022.116804
DO - 10.1016/j.trac.2022.116804
M3 - Review article
AN - SCOPUS:85140768341
SN - 0165-9936
VL - 157
SP - 1
EP - 8
JO - TrAC - Trends in Analytical Chemistry
JF - TrAC - Trends in Analytical Chemistry
M1 - 116804
ER -