Deep learning for near-infrared spectral data modelling: Hypes and benefits
2022
Mishra, Puneet | Passos, Dário | Marini, Federico | Xu, Junli | Amigo, Jose | Gowen, Aoife | Jansen, Jeroen | Biancolillo, Alessandra | Roger, Jean-Michel | Rutledge, Douglas | Nordon, Alison | Wageningen University and Research [Wageningen] (WUR) | University of Algarve [Portugal] | Università degli Studi di Roma "La Sapienza" = Sapienza University [Rome] (UNIROMA) | University College Dublin [Dublin] (UCD) | Ikerbasque - Basque Foundation for Science | Universidad del País Vasco [Espainia] / Euskal Herriko Unibertsitatea [España] = University of the Basque Country [Spain] = Université du pays basque [Espagne] (UPV / EHU) | Radboud University [Nijmegen] | Università degli Studi dell'Aquila = University of L'Aquila = Université de L'Aquila (UNIVAQ) | Technologies et Méthodes pour les Agricultures de demain (UMR ITAP) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier ; Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) | Charles Sturt University [Australia] | WestCHEM ; School of Chemistry ; University of Glasgow-University of Glasgow | FCT - Fundação para a Ciência e a Tecnologia, Portugal, for funding CEOT project UIDB/00631/2020 CEOT BASE and UIDP/00631/2020 CEOT PROGRAMÁTICO.
International audience
显示更多 [+] 显示较少 [-]英语. 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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