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dc.creatorLópez-Maestresalas, Ainaraes_ES
dc.creatorPérez Roncal, Claudiaes_ES
dc.creatorTierno, Robertoes_ES
dc.creatorArazuri Garín, Silviaes_ES
dc.creatorRuiz de Galarreta, José Ignacioes_ES
dc.creatorJarén Ceballos, Carmenes_ES
dc.date.accessioned2018-12-16T19:45:09Z
dc.date.available2018-12-16T19:45:09Z
dc.date.issued2017
dc.identifier.isbn978-88-95608-52-5
dc.identifier.issn2283-9216
dc.identifier.urihttps://hdl.handle.net/2454/31830
dc.description.abstractPotato (Solanum tuberosum, L) compounds are generally determined by analytical methods including gasliquid chromatography (GLC), HPLC and UV-VIS spectrophotometry. These methods require a lot of time and are destructive. Therefore, they seem to be not suitable for in-line applications in the food industry. Nearinfrared spectroscopy (NIRS) is a technique that presents some advantages over reference methods for quantitative analysis of agricultural and food products since it is fast, reliable and non-destructive. For this reason, in this study, quantitative analyses were carried out to determine main compounds in potatoes using NIRS. Potato tubers grown in two consecutive years were used for the analyses. NIR spectral acquisition was acquired on lyophilized samples. In year 1, a total of 135 samples were used while 228 samples were used in year 2. Lyophilized samples were also scanned by NIRS, two replicates per samples were acquired and the mean spectrum of each sample was used for the analysis. Different chemical analyses were carried out each year. Thus, in year 1 the following parameters were quantified: reducing sugars (RS) and nitrogen (N), whereas in year 2, total soluble phenolics (TSP) and hydrophilic antioxidant capacity (HAC) were extracted and quantified. Then, chemometric analyses were performed using Unscrambler X (version 10.3, CAMO software AS, Oslo, Norway) to correlate wet chemical analysis with spectral data. Quantitative analyses based on PLS regression models were developed in order to predict the above chemical compounds of tubers in a non-destructive manner. Good PLS regression models were obtained for the prediction of nitrogen and TSP with coefficients of determination (R2) above 0.83. Moreover, PLS models obtained for the estimation of HAC could be used for screening and approximate calibrations.en
dc.description.sponsorshipThis work was financed within the frame of INIA’s project RTA2013-00006-C03-01-03, the Basque Government and the Universidad Pública de Navarra through the concession of a predoctoral research grant.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherAIDICit
dc.relation.ispartofChemical Engineering Transactions, vol. 58, 2017en
dc.rights© 2017 AIDIC Servizi S.r.l.it
dc.subjectPotato compoundsen
dc.subjectNIRSen
dc.titlePrediction of main potato compounds by NIRSen
dc.typeArtículo / Artikuluaes
dc.typeinfo:eu-repo/semantics/articleen
dc.contributor.departmentUniversidad Pública de Navarra. Departamento de Proyectos e Ingeniería Rurales_ES
dc.contributor.departmentNafarroako Unibertsitate Publikoa. Landa Ingeniaritza eta Proiektuak Sailaeu
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.doi10.3303/CET1758065
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/1PE/RTA2013-00006en
dc.relation.publisherversionhttps://doi.org/10.3303/CET1758065
dc.type.versionVersión publicada / Argitaratu den bertsioaes
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.contributor.funderUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoaes


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