Prediction of ‘Nules Clementine’ mandarin susceptibility to rind breakdown disorder using Vis/NIR spectroscopy

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dc.contributor.authorMagwaza, Lembe S.en_ZA
dc.contributor.authorOpara, Umezuruike Linusen_ZA
dc.contributor.authorTerry, Leon A.en_ZA
dc.contributor.authorLandahl, Sandraen_ZA
dc.contributor.authorCronje, Paul J.en_ZA
dc.contributor.authorNieuwoudt, Heleneen_ZA
dc.contributor.authorMouazen, Abdul Mounemen_ZA
dc.contributor.authorSaeys, Wouteren_ZA
dc.contributor.authorNicolai, Bart M.en_ZA
dc.date.accessioned2012-08-07T13:11:43Z
dc.date.issued2012-12
dc.descriptionThe original publication is available at http://www.sciencedirect.com/en_ZA
dc.description.abstractThe use of diffuse reflectance visible and near infrared (Vis/NIR) spectroscopy was explored as a nondestructive technique to predict ‘Nules Clementine’ mandarin fruit susceptibility to rind breakdown (RBD) disorder by detecting rind physico-chemical properties of 80 intact fruit harvested from different canopy positions. Vis/NIR spectra were obtained using a LabSpec® spectrophotometer. Reference physicochemical data of the fruit were obtained after 8 weeks of storage at 8 ◦C using conventional methods and included RBD, hue angle, colour index, mass loss, rind dry matter, as well as carbohydrates (sucrose, glucose, fructose, total carbohydrates), and total phenolic acid concentrations. Principal component analysis (PCA) was applied to analyse spectral data to identify clusters in the PCA score plots and outliers. Partial least squares (PLS) regression was applied to spectral data after PCA to develop prediction models for each quality attribute. The spectra were subjected to a test set validation by dividing the data into calibration (n = 48) and test validation (n = 32) sets. An extra set of 40 fruit harvested from a different part of the orchard was used for external validation. PLS-discriminant analysis (PLS-DA) models were developed to sort fruit based on canopy position and RBD susceptibility. Fruit position within the canopy had a significant influence on rind biochemical properties. Outside fruit had higher rind carbohydrates, phenolic acids and dry matter content and lower RBD index than inside fruit. The data distribution in the PCA and PLS-DA models displayed four clusters that could easily be identified. These clusters allowed distinction between fruit from different preharvest treatments. NIR calibration and validation results demonstrated that colour index, dry matter, total carbohydrates and mass loss were predicted with significant accuracy, with residual predictive deviation (RPD) for prediction of 3.83, 3.58, 3.15 and 2.61, respectively. The good correlation between spectral information and carbohydrate content demonstrated the potential of Vis/NIR as a non-destructive tool to predict fruit susceptibility to RBD.en_ZA
dc.description.versionPublishers' Versionen_ZA
dc.embargo.lift2050-12-31
dc.embargo.terms2050-12-31en_ZA
dc.format.extentp. 1-10 : ill. (some col.)en_ZA
dc.identifier.citationMagwasa, L. S. et al 2012. Prediction of ‘Nules Clementine’ mandarin susceptibility to rind breakdown disorder using Vis/NIR spectroscopy. Postharvest Biology and Technology, 74, 1-10, doi:10.1016/j.postharvbio.2012.06.007.en_ZA
dc.identifier.otherdoi:10.1016/j.postharvbio.2012.06.007
dc.identifier.urihttp://hdl.handle.net/10019.1/22025
dc.language.isoen_ZAen_ZA
dc.publisherElsevieren_ZA
dc.rights.holderElsevieren_ZA
dc.subjectNon-destructive techniqueen_ZA
dc.subjectVis/NIR infrared spectroscopyen_ZA
dc.subjectRind breakdown disorder (RBD)en_ZA
dc.subject‘Nules Clementine’ mandarin -- Postharvest physiologyen_ZA
dc.titlePrediction of ‘Nules Clementine’ mandarin susceptibility to rind breakdown disorder using Vis/NIR spectroscopyen_ZA
dc.typeArticleen_ZA
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