Repository logo
 
Publication

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

2022-07Journal article Restricted access
Simple item page
dc.contributor.authorYang, Jie
dc.contributor.authorLuo, Xuan
dc.contributor.authorZhang, Xiaolei
dc.contributor.authorPassos, Dário
dc.contributor.authorXie, Lijuan
dc.contributor.authorRao, Xiuqin
dc.contributor.authorXu, Huirong
dc.contributor.authorTing, K.C.
dc.contributor.authorLin, Tao
dc.contributor.authorYing, Yibin
dc.date.accessioned2022-12-07T10:12:38Z
dc.date.available2022-12-07T10:12:38Z
dc.date.issued2022-07
dc.description.abstractModel 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 linear models. This study proposes a model fine-tuning approach to update nonlinear deep learning models using limited sample sizes for fruit detection under interseason variation. This approach provides RMSE of 0.407, 1.035, and 0.642, for predicting soluble solid content (%) or dry matter content (%), in the Cuiguan pear, Rocha pear, and Mango dataset. The proposed approach reduces at least 9.2%, 17.5%, and 11.6% of test RMSE in three datasets compared with conventional model updating methods, including the global model, recalibration, and slope/bias correction. The model fine-tuning approach shows improved reliability under different updating sample sizes, ranging from 5% to 20% proportions of the new season's samples. The utilization of cumulative data in multiple previous seasons enables further improved performance. This study potentially facilitates implementing high-performance deep learning approaches in on-site applications of fruit quality control.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.foodcont.2022.109108pt_PT
dc.identifier.issn0956-7135
dc.identifier.urihttp://hdl.handle.net/10400.1/18594
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationCenter for Electronics, Optoelectronics and Telecommunications
dc.relationCenter for Electronics, Optoelectronics and Telecommunications
dc.subjectBiological variabilitypt_PT
dc.subjectVisible/near-infrared spectroscopypt_PT
dc.subjectDeep learningpt_PT
dc.subjectConvolutional neural networkpt_PT
dc.subjectModel updatingpt_PT
dc.subjectFruit qualitypt_PT
dc.titleA deep learning approach to improving spectral analysis of fruit quality under interseason variationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCenter for Electronics, Optoelectronics and Telecommunications
oaire.awardTitleCenter for Electronics, Optoelectronics and Telecommunications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00631%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00631%2F2020/PT
oaire.citation.startPage109108pt_PT
oaire.citation.titleFood Controlpt_PT
oaire.citation.volume140pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNamePassos
person.givenNameDário
person.identifier324764
person.identifier.ciencia-id3D13-C289-0595
person.identifier.orcid0000-0002-5345-5119
person.identifier.scopus-author-id21743737200
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication30c8500a-c12b-47c3-845e-9b64957cf233
relation.isAuthorOfPublication.latestForDiscovery30c8500a-c12b-47c3-845e-9b64957cf233
relation.isProjectOfPublication6c1217d9-1340-45e8-91d8-e75348854f62
relation.isProjectOfPublication77b70459-1e8c-4a6c-9856-58860aaddb6b
relation.isProjectOfPublication.latestForDiscovery6c1217d9-1340-45e8-91d8-e75348854f62

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
A deep learning approach to improving spectral analysis of fruit quality under interseason variation.pdf
Size:
5.02 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
3.46 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

FCT2-Artigos (em revistas ou actas indexadas)