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Multi-output 1-dimensional convolutional neural networks for simultaneous prediction of different traits of fruit based on near-infrared spectroscopy

2022-01Journal article Open access
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dc.contributor.authorMishra, Puneet
dc.contributor.authorPassos, Dário
dc.date.accessioned2021-10-29T13:03:07Z
dc.date.available2021-10-29T13:03:07Z
dc.date.issued2022-01
dc.description.abstractIn spectral data predictive modelling of fresh fruit, often the models are calibrated to predict multiple responses. A common method to deal with such a multi-response predictive modelling is the partial least-squares (PLS2) regression. Recently, deep learning (DL) has shown to outperform partial least-squares (PLS) approaches for single fruit traits prediction. The DL can also be adapted to perform multi-response modelling. This study presents an implementation of DL modelling for multi-response prediction for spectral data of fresh fruit. To show this, a real NIR data set related to SSC and MC measurements in pear fruit was used. Since DL models perform better with larger data sets, a data augmentation procedure was performed prior to data modelling. Furthermore, a comparative study was also performed between two of the most used DL architectures for spectral analysis, their multi-output and single-output variants and a classic baseline model using PLS2. A key point to note that all the DL modelling presented in this study is performed using novel automated optimisation tools such as Bayesian optimisation and Hyperband. The results showed that DL models can be easily adapted by changing the output of the fully connected layers to perform multi-response modelling. In comparison to the PLS2, the multi-response DL model showed ~13 % lower root mean squared error (RMSE), showing the ease and superiority of handling multi-response by DL models for spectral calibration.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.postharvbio.2021.111741pt_PT
dc.identifier.eissn1873-2356
dc.identifier.urihttp://hdl.handle.net/10400.1/17263
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectSpectroscopypt_PT
dc.subjectChemometricspt_PT
dc.subjectCalibrationpt_PT
dc.subjectChemistrypt_PT
dc.titleMulti-output 1-dimensional convolutional neural networks for simultaneous prediction of different traits of fruit based on near-infrared spectroscopypt_PT
dc.title.alternativeRedes neurais convolucionais multidimensionais de saída para previsão simultânea de diferentes traços de frutas com base em espectroscopia quase infravermelhapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.startPage111741pt_PT
oaire.citation.titlePostharvest Biology and Technologypt_PT
oaire.citation.volume183pt_PT
person.familyNamePassos
person.givenNameDário
person.identifier324764
person.identifier.ciencia-id3D13-C289-0595
person.identifier.orcid0000-0002-5345-5119
person.identifier.scopus-author-id21743737200
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication30c8500a-c12b-47c3-845e-9b64957cf233
relation.isAuthorOfPublication.latestForDiscovery30c8500a-c12b-47c3-845e-9b64957cf233

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