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A TSS classification study of 'Rocha' pear (Pyrus communis L.) based on non-invasive visible/near infra-red reflectance spectra

2017-10Journal article Restricted access
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dc.contributor.authorBexiga, Florentino
dc.contributor.authorRodrigues, Daniela
dc.contributor.authorGuerra, Rui Manuel Farinha das Neves
dc.contributor.authorBrazio, António
dc.contributor.authorBalegas, Tiago
dc.contributor.authorCavaco, A. M.
dc.contributor.authorAntunes, Maria Dulce
dc.contributor.authorValente de Oliveira, JOSÉ
dc.date.accessioned2019-11-20T15:07:20Z
dc.date.available2019-11-20T15:07:20Z
dc.date.issued2017-10
dc.description.abstractThe study focuses on the application of machine learning techniques for classifying the internal quality of 'Rocha' Pear (Pyrus communis L.), i.e., the total soluble solids (TSS), using the non-invasive technique of visible/near infra-red reflectance spectroscopy. Six representative classifiers were evaluated under realistic experimental conditions. The classifiers include representatives of classic parametric (logistic and multiple linear regression), non-parametric distance based methods (K-nearest neighbors), correlation-based (partial least squares), ensemble methods (random forests) and maximum margin classifiers (support vector machines). The classifiers were assessed against metrics such as accuracy, Cohen's Kappa, F-Measure, and the area under the precision recall curve (AUC) in a 10 x 10-fold cross-validation plan. For result analysis non-parametric statistical test of hypotheses were employed. A total of 4880 fruit samples from different origins, maturation states, and harvest years were considered. The main conclusion is that the maximum margin classifier outperforms all the others studied ones, including the commonly used partial least squares. The conclusion holds for both a reflectance spectrum with 1024 features and for a 128 subsample of these. An estimate of the out-of-sample performance for the best classifier is also provided.
dc.description.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1016/j.postharvbio.2017.05.014
dc.identifier.issn0925-5214
dc.identifier.issn1873-2356
dc.identifier.urihttp://hdl.handle.net/10400.1/12988
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier Science
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSpectroscopy
dc.subjectQuality
dc.subjectFruit
dc.titleA TSS classification study of 'Rocha' pear (Pyrus communis L.) based on non-invasive visible/near infra-red reflectance spectra
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage30
oaire.citation.startPage23
oaire.citation.titlePostharvest Biology and Technology
oaire.citation.volume132
person.familyNameRodrigues
person.familyNameGuerra
person.familyNameBrazio
person.familyNameBalegas
person.familyNameCavaco Guerra
person.familyNameAntunes
person.familyNameLUÍS VALENTE DE OLIVEIRA
person.givenNameDaniela
person.givenNameRui
person.givenNameAntónio
person.givenNameTiago
person.givenNameAna Margarida
person.givenNameMaria Dulce
person.givenNameJOSÉ
person.identifierC-1285-2012
person.identifier177556
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person.identifier.ciencia-idC11B-9B05-217E
person.identifier.ciencia-id1F12-C1D3-7717
person.identifier.orcid0000-0003-3659-099X
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person.identifier.orcid0000-0002-7675-0719
person.identifier.orcid0000-0002-9801-8385
person.identifier.orcid0000-0003-2708-5991
person.identifier.orcid0000-0002-8913-6136
person.identifier.orcid0000-0001-5337-5699
person.identifier.ridV-3842-2018
person.identifier.ridA-4683-2012
person.identifier.scopus-author-id57194445681
person.identifier.scopus-author-id6602899707
person.identifier.scopus-author-id7102645075
rcaap.rightsrestrictedAccess
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