Zur Kurzanzeige

dc.contributor.authorCastro Reigía, David
dc.contributor.authorGarcía, Inmaculada
dc.contributor.authorSanllorente Méndez, Silvia 
dc.contributor.authorSarabia Peinador, Luis Antonio 
dc.contributor.authorOrtiz Fernández, Mª Cruz 
dc.date.accessioned2025-01-17T14:12:45Z
dc.date.available2025-01-17T14:12:45Z
dc.date.issued2024-11
dc.identifier.issn0026-265X
dc.identifier.urihttp://hdl.handle.net/10259/9961
dc.description.abstractThis paper deals with the application of near infrared spectroscopy (NIR) in a classification problem involving multiple classes in order to differentiate contaminated olives. A total of 452 samples, ripe and unripe, were treated with five different agrochemicals reproducing the traditional fumigation process in the olive tree. The main objective was to differentiate through a classification if the samples were or were not treated, but also, which chemical was used for each olive. Firstly, Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to differentiate between untreated and treated samples. Then, two novel chemometric approaches, a classification one and a modelling one, were applied for ripe and unripe olives, achieving good results and determining with which chemical were the olives sprinkled with. For the classification of the samples in the six different classes (untreated olives, or treated with one of the five agrochemicals), an Automatic Hierarchical Model Builder (AHIMBU) was used, applying sequential binary PLS-DAs. Nevertheless, for the modelling approach, a compliant model, PLS2-CM, also based on PLS, was used with two different codifications for the classes: i) the classic and well-known One Versus All (OVA), and ii) the Error Correction Output Code (ECOC) optimal matrix. The final global results were evaluated using the Diagonal Modified Confusion Entropy (DMCEN) index, which ranges between 0 and 1, and is very sensitive to changes in the sensitivity–specificity matrices (note that the lower the DMCEN, the better the classification is). The best DMCEN value in prediction for unripe olives, 0.4898, was obtained for the PLS2-CM-ECOC, while 0.6937 and 0.7705 DMCEN values were obtained for AHIMBU and PLS2-CM-OVA, respectively. For the case of the ripe samples, the DMCEN values in prediction were better than the ones for the unripe olives: 0.6016, 0.5051, and 0.4166, for AHIMBU, PLS2-CM-OVA and PLS2-CM-ECOC, respectively. In every case, the best DMCEN has been obtained with the PLS2-CM-ECOC procedure.en
dc.description.sponsorshipThis work has been funded by the Ministerio de Industria, Comercio y Turismo under Project ESPECTROLIVE (AEI-010500–2023-232).en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofMicrochemical Journal. 2024, V. 206, 111550es
dc.rightsAtribución-NoComercial 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectECOCen
dc.subjectPLS-DAen
dc.subjectAIHMBUen
dc.subjectDMCENen
dc.subjectNIR spectroscopyen
dc.subjectSensitivityen
dc.subjectSpecificityen
dc.subject.otherQuímicaes
dc.subject.otherChemistryen
dc.subject.otherQuímica analíticaes
dc.subject.otherChemistry, Analyticen
dc.subject.otherAlimentoses
dc.subject.otherFooden
dc.titleDifferentiating five agrochemicals used in the treatment of intact olives by means of NIR spectroscopy, discriminant analysis and compliant class modelsen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1016/j.microc.2024.111550es
dc.identifier.doi10.1016/j.microc.2024.111550
dc.journal.titleMicrochemical Journalen
dc.volume.number206es
dc.page.initial111550es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


Dateien zu dieser Ressource

Thumbnail

Das Dokument erscheint in:

Zur Kurzanzeige