Near infrared spectroscopy models to predict sensory and texture traits of sweetpotato roots

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cg.authorship.typesCGIAR and developing country instituteen
cg.authorship.typesCGIAR and advanced research instituteen
cg.contributor.affiliationInternational Potato Centeren
cg.contributor.affiliationCentre de Coopération Internationale en Recherche Agronomique Pour le Développementen
cg.contributor.affiliationInternational Institute of Tropical Agricultureen
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.donorBill & Melinda Gates Foundationen
cg.coverage.countryUganda
cg.coverage.iso3166-alpha2UG
cg.coverage.regionAfrica
cg.creator.identifierJudith Nantongo: 0000-0001-7914-9139en
cg.creator.identifierMariam Nakitto: 0000-0002-4140-7216en
cg.creator.identifierGabriela Burgos Zapata: 0000-0002-0268-6785en
cg.creator.identifierRobert O.M. Mwanga: 0000-0002-3166-0015en
cg.creator.identifierReuben SSALI Tendo: 0000-0002-8143-6564en
cg.creator.identifierALAMU Emmanuel Oladeji (PhD, FIFST, MNIFST): 0000-0001-6263-1359en
cg.howPublishedFormally Publisheden
cg.identifier.doihttps://doi.org/10.1177/09670335241259901en
cg.isijournalISI Journalen
cg.issn1751-6552en
cg.issue3en
cg.journalJournal of Near Infrared Spectroscopyen
cg.reviewStatusPeer Reviewen
cg.subject.actionAreaGenetic Innovation
cg.subject.actionAreaResilient Agrifood Systems
cg.subject.cipBREEDINGen
cg.subject.cipFOOD SECURITYen
cg.subject.cipSWEETPOTATOESen
cg.subject.cipSWEETPOTATO AGRI-FOOD SYSTEMSen
cg.subject.cipCROP AND SEED SYSTEMS SCIENCES CSSen
cg.subject.impactAreaNutrition, health and food security
cg.subject.sdgSDG 2 - Zero hungeren
cg.subject.sdgSDG 9 - Industry, innovation and infrastructureen
cg.volume32en
dc.contributor.authorNantongo, J.S.en
dc.contributor.authorSerunkuma, E.en
dc.contributor.authorDavrieux, F.en
dc.contributor.authorNakitto, M.en
dc.contributor.authorBurgos, G.en
dc.contributor.authorThomas, Z.F.en
dc.contributor.authorEduardo, P.en
dc.contributor.authorCarey, T.en
dc.contributor.authorSwankaert, J.en
dc.contributor.authorMwanga, R.O.M.en
dc.contributor.authorAlamu, E.O.en
dc.contributor.authorSsali, R.T.en
dc.date.accessioned2024-06-24T15:29:06Zen
dc.date.available2024-06-24T15:29:06Zen
dc.identifier.urihttps://hdl.handle.net/10568/148687
dc.titleNear infrared spectroscopy models to predict sensory and texture traits of sweetpotato rootsen
dcterms.abstractHigh-throughput phenotyping technologies successfully employed in plant breeding and precision agriculture could facilitate the screening process for developing consumer-preferred traits. The current study evaluated the potential of near infrared (NIR) spectroscopy to predict visual, aromatic, flavor, taste and texture traits of sweetpotatoes. The focus was to develop predicting models that would be cost-effective, efficient and high throughput. The roots of 207 sweetpotato genotypes from six agroecological zones of Uganda were collected from breeding trials. The spectra were collected in the wavelengths of 400 – 2500 nm at 2 nm intervals. Using the plsR package, the calibrations were carried out using external validation models. The best calibration equation between the sensory and texture reference values (10-point scales) and spectral data was identified based on the highest coefficient of determination (R2) and smallest RMSE in calibration and validation. Of the visual traits, orange color intensity was well calibrated using NIR spectroscopy (R2val = 0.92, SEP = 0.92), and the model is sufficient for field application. Pumpkin aroma (R2val = 0.67, SEP = 0.33) was the highest predicted among the aromas. The pumpkin flavour model exhibited the highest coefficient of determination in the calibration (R2val = 0.52, SEP = 0.45) for the traits considered under flavor and taste. Different models for textural traits exhibited moderate calibration coefficients: mealiness (chalky/floury) by hand (R2val = 0.75; SEP = 1.31), crumbliness (R2val = 0.73, SEP = 1.21), moisture in mass (R2val = 0.73, SEP = 1.26), fracturability (R2val = 0.60, SEP = 1.52), hardness by hand (R2val = 0.61, SEP = 1.27) and dry matter (R2val = 0.70, SEP = 3.10). The range error ratio (RER) values were mostly >6.0. These models could be used for preliminary screening. The predictability of the traits varied among different modes of samples. Models could be improved with an increased range of reference values and/or exploiting the correlations between chemical compounds and sensory traits.en
dcterms.accessRightsLimited Access
dcterms.audienceAcademicsen
dcterms.audienceCGIARen
dcterms.audienceDevelopment Practitionersen
dcterms.audienceDonorsen
dcterms.audienceExtensionen
dcterms.audienceFarmersen
dcterms.audienceGeneral Publicen
dcterms.audienceNGOsen
dcterms.audiencePolicy Makersen
dcterms.audienceScientistsen
dcterms.available2024-06-14en
dcterms.bibliographicCitationNantongo, J.S.; Serunkuma, E.; Davrieux, F.; Nakitto, M.; Burgos, G.; Thomas, Z.F.; Eduardo, P.; Carey, T.; Swankaert, J.; Mwanga, R.O.; Alamu, E.O.; Ssali, R. 2024. Near infrared spectroscopy models to predict sensory and texture traits of sweetpotato roots. Journal of Near Infrared Spectroscopy. ISSN 1751-6552. https://doi.org/10.1177/09670335241259901en
dcterms.extentpp. 93-104en
dcterms.issued2024-06-14en
dcterms.languageen
dcterms.licenseCopyrighted; all rights reserved
dcterms.publisherSAGE Publicationsen
dcterms.subjectbreedingen
dcterms.subjectconsumer behaviouren
dcterms.subjectfood securityen
dcterms.subjectsweet potatoesen
dcterms.subjectphenotypingen
dcterms.subjectspectroscopyen
dcterms.typeJournal Article

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