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dc.contributor.authorBorraz Martínez, Sergio
dc.contributor.authorBoqué, Ricard
dc.contributor.authorSimó Cruanyes, Joan
dc.contributor.authorMestre, Mariàngela
dc.contributor.authorGras Moreu, Anna Maria
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Tecnologia Agroalimentària i Biotecnologia
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Agroalimentària i Biotecnologia
dc.date.accessioned2020-03-23T12:09:50Z
dc.date.available2021-06-05T00:27:23Z
dc.date.issued2019-11-01
dc.identifier.citationBorraz, S. [et al.]. Development of a methodology to analyze leaves from Prunus dulcis varieties using near infrared spectroscopy. "Talanta", 1 Novembre 2019, vol. 204, p. 320-328.
dc.identifier.issn0039-9140
dc.identifier.urihttp://hdl.handle.net/2117/180898
dc.description.abstractNear-infrared spectroscopy (NIRS) can be a faster and more economical alternative to traditional methods for screening varietal mixtures of nursery plants during the propagation process to ensure varietal purity and to avoid errors in the dispatch batches. The global objective of this work was to develop and optimize a NIR spectral collection method for construction of robust multivariate discrimination models. Three different varieties of Prunus dulcis (Avijor, Guara, and Pentacebas) of agricultural interest were used for this study. Sources of variation were investigated, including the position of the leaves on the trees, differences among trees of the same variety, and differences at the varietal level. Three types of processed samples were investigated. Fresh leaves, dried leaves, and dried leaves in powder form were included in each analysis. A study of spectral pre-treatment methods was also performed, and multivariate methods were applied to analyze the influence of different factors on classification. These included principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and ANOVA simultaneous component analysis (ASCA). The results indicated that variety was the most important factor for classification. The spectral pre-treatment that provided the best results was a combination of standard normal variate (SNV), Savitzky-Golay first derivative, and mean-centering methods. With regard to the type of processed sample, the highest percentages of correct classifications were obtained with fresh and dried powdered leaves at both the training set and test set validation levels. This study represents the first step towards the consolidation of NIRS as a method to identify Prunus dulcis varieties.
dc.format.extent9 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria agroalimentària
dc.subject.lcshFoliar diagnosis
dc.subject.lcshAlmond
dc.subject.otherOptimization
dc.subject.otherAlmond trees
dc.subject.otherLeaf analysis
dc.subject.otherVarietal purity
dc.subject.otherNIR
dc.subject.otherPLS-DA
dc.titleDevelopment of a methodology to analyze leaves from Prunus dulcis varieties using near infrared spectroscopy
dc.typeArticle
dc.subject.lemacFulles
dc.subject.lemacAmetller
dc.contributor.groupUniversitat Politècnica de Catalunya. MVCO - Millora Vegetal de Caràcters Organolèptics
dc.contributor.groupUniversitat Politècnica de Catalunya. BIOCOM-SC - Grup de Biologia Computacional i Sistemes Complexos
dc.identifier.doi10.1016/j.talanta.2019.05.105
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0039914019305995
dc.rights.accessOpen Access
local.identifier.drac25312500
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/Spain/MINECO/AGL 2015- 70106-R
local.citation.authorBorraz, S.; Boqué, R.; Simo, J.; Mestre, M.; Gras, A.
local.citation.publicationNameTalanta
local.citation.volume204
local.citation.startingPage320
local.citation.endingPage328


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Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain