A deep learning approach to improving spectral analysis of fruit quality under interseason variation

Autor: Jie Yang, Xuan Luo, Xiaolei Zhang, Dário Passos, Lijuan Xie, Xiuqin Rao, Huirong Xu, K.C. Ting, Tao Lin, Yibin Ying

Model updating for developed calibrations is critical for robust spectral analysis in fruit quality control. Existing methods have limitations that usually need sufficient samples for model recalibration and are mainly designed for conventional linea

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e537895704d9d4c6ad1709c1f5be0220
https://hdl.handle.net/10400.1/18594

A tutorial on automatic hyperparameter tuning of deep spectral modelling for regression and classification tasks

Autor: Dário Passos, Given Names Deactivated Family Name Deactivated

Publikováno v: Chemometrics and Intelligent Laboratory Systems, 223
Chemometrics and Intelligent Laboratory Systems 223 (2022)

Deep spectral modelling for regression and classification is gaining popularity in the chemometrics domain. A major topic in the deep learning (DL) modelling of spectral data is the choice and optimization of the deep neural network architecture suit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb62434825f2127250acda8f291b6cce
https://research.wur.nl/en/publications/a-tutorial-on-automatic-hyperparameter-tuning-of-deep-spectral-mo

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

Autor: 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

Publikováno v: TrAC-Trends in Analytical Chemistry 157 (2022)
Trac-Trends in Analytical Chemistry, 157, 1-8
Trac-Trends in Analytical Chemistry, 157, pp. 1-8
TrAC-Trends in Analytical Chemistry, 157

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 da

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::462cdccb1a0c0e0db9abb5aa881dc476
https://doi.org/10.1016/j.trac.2022.116804

Nondestructive Assessment of Citrus Fruit Quality and Ripening by Visible–Near Infrared Reflectance Spectroscopy

Autor: Ana M. Cavaco, Rui Guerra, Rosa Pires, Dário Passos, Maria Dulce Antunes

As non-climacteric, citrus fruit are only harvested at their optimal edible ripening stage. The usual approach followed by producers and packinghouses to establish the internal quality and ripening of citrus fruit is to collect fruit sets throughout

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94ada10bee683ea440afde7b43ea855f
http://www.intechopen.com/articles/show/title/nondestructive-assessment-of-citrus-fruit-quality-and-ripening-by-visible-near-infrared-reflectance-

An automated deep learning pipeline based on advanced optimisations for leveraging spectral classification modelling

Autor: Puneet Mishra, Dário Passos

Publikováno v: Chemometrics and Intelligent Laboratory Systems, 215
Chemometrics and Intelligent Laboratory Systems 215 (2021)
Repositório Científico de Acesso Aberto de Portugal
Repositório Científico de Acesso Aberto de Portugal (RCAAP)
instacron:RCAAP

Na modelagem de deep learning (DL) para dados espectrais, um grande desafio está relacionado à escolha da arquitetura de rede DL e à seleção dos melhores hiperparmetros. Muitas vezes, pequenas mudanças na arquitetura neural ou seu hiperparômet

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::afe9e389aec58c85d2a951f2805c1029
https://doi.org/10.1016/j.chemolab.2021.104354