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Journal Articles Trends in Analytical Chemistry Year : 2022

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

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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.
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Dates and versions

hal-03889114 , version 1 (07-12-2022)

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Puneet Mishra, Dário Passos, Federico Marini, Junli Xu, Jose Amigo, et al.. Deep learning for near-infrared spectral data modelling: Hypes and benefits. Trends in Analytical Chemistry, 2022, 157, pp.116804. ⟨10.1016/j.trac.2022.116804⟩. ⟨hal-03889114⟩
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