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'Rocha' pear firmness predicted by a Vis/NIR segmented model

2009Journal article Restricted access
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dc.contributor.authorCavaco, A. M.
dc.contributor.authorPinto, Patricia IS
dc.contributor.authorAntunes, Maria Dulce
dc.contributor.authorda Silva, J. M.
dc.contributor.authorGuerra, Rui Manuel Farinha das Neves
dc.date.accessioned2015-06-19T13:47:33Z
dc.date.available2015-06-19T13:47:33Z
dc.date.issued2009
dc.description.abstractWe present a segmented partial least squares (PLS) prediction model for firmness of 'Rocha' pear (Pyres communis L) during fruit ripening under shelf-life conditions. Pears were collected from three different orchards. Orchard I provided the pears for model calibration and internal validation (set 1). These were transferred to shelf-life in the dark at 20 +/- 2 degrees C and 70% RH, immediately after harvest. External validation was performed on the pears from the other two orchards (sets 2 and 3), which were stored under different conditions before shelf-life. Fruit was followed in the shelf-life period by visible/near infrared reflectance spectroscopy (Vis/NIRS) in the range 400-950 nm. The correlation between firmness and the reflectance at some wavelength bands was markedly different depending on ripening stage. A segmented partial least squares model was then constructed to predict firmness. This PLS model has two segments: (1) unripe and ripening/ripe pears (high firmness); (2) over-ripe pears (low firmness). The prediction is done in two steps. First, a full range model (full model) is applied. When the full model prediction gives a low firmness value, then the over-ripe model is applied to refine the prediction. The full model is reasonably significant in regression terms, robust, but allows only a coarse quantitative prediction (standard deviation ratio, SDR = 2.48, 1.50 and 2.40 for sets 1, 2 and 3, respectively). Also, RMSEP% = 139%, 91% and 56%, indicating large relative errors at low firmness values. The segmented model improved moderately the correlation, and the values of RMSEC, RMSEP and SDR: it improved significantly the RMSEP% (29%, 55% and 31%), providing an improvement of the relative prediction errors at low firmness values. This method improves the ordinary PLS models. Finally, we tested whether chlorophyll alone was enough for a predictive model for firmness, but the results showed that the absorption of chlorophyll alone does not explain the performance of the PLS models. (C) 2008 Elsevier B.V. All rights reserved.
dc.identifier.doihttps://dx.doi.org/10.1016/j.postharvbio.2008.08.013
dc.identifier.issn0925-5214
dc.identifier.otherAUT: ACA01304; MAN00114; RGU01166;
dc.identifier.urihttp://hdl.handle.net/10400.1/6488
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relation.isbasedonP-003-N1W
dc.title'Rocha' pear firmness predicted by a Vis/NIR segmented model
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage319
oaire.citation.startPage311
oaire.citation.titlePostharvest Biology and Technology
oaire.citation.volume51
person.familyNameCavaco Guerra
person.familyNamePinto
person.familyNameAntunes
person.familyNameGuerra
person.givenNameAna Margarida
person.givenNamePatricia IS
person.givenNameMaria Dulce
person.givenNameRui
person.identifierC-1285-2012
person.identifier643457
person.identifier177556
person.identifier.ciencia-idC91E-B434-E327
person.identifier.ciencia-idE51D-5CCB-B1C6
person.identifier.ciencia-idC11B-9B05-217E
person.identifier.ciencia-id3D16-5067-D6BB
person.identifier.orcid0000-0003-2708-5991
person.identifier.orcid0000-0001-7854-3898
person.identifier.orcid0000-0002-8913-6136
person.identifier.orcid0000-0002-8642-5792
person.identifier.ridM-3817-2013
person.identifier.ridA-4683-2012
person.identifier.scopus-author-id6602899707
person.identifier.scopus-author-id10240774300
person.identifier.scopus-author-id7102645075
rcaap.rightsrestrictedAccess
rcaap.typearticle
relation.isAuthorOfPublication18ad736f-b1f3-4a19-9636-e7a80248cf94
relation.isAuthorOfPublication64bc526e-5281-44e5-91eb-5743436109f9
relation.isAuthorOfPublication7947cc50-4ae0-4ada-8ddf-081f247adc90
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relation.isAuthorOfPublication.latestForDiscovery7947cc50-4ae0-4ada-8ddf-081f247adc90

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