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Development and validation of near-infrared spectroscopy procedures for prediction of cassava root dry matter and amylose contents in Ugandan cassava germplasm

dc.contributor.authorNuwamanya, E.
dc.contributor.authorWembabazi, E.
dc.contributor.authorKanaabi, M.
dc.contributor.authorNamakula, F.B.
dc.contributor.authorKatungisa, A.
dc.contributor.authorLyatumi, I.
dc.contributor.authorEzuma, W.
dc.contributor.authorAlamu, E.O.
dc.contributor.authorDufour, D.
dc.contributor.authorKawuki, R.
dc.contributor.authorDavrieux, F.
dc.date.accessioned2023-10-23T10:40:54Z
dc.date.available2023-10-23T10:40:54Z
dc.date.issued2023-09-04
dc.identifier.citationNuwamanya, E.,Wembabazi, E., Kanaabi, M., Namakula, F.B., Katungisa, A., Lyatumi, I., ... & Devrieux, F. (2023). Development and validation of near‐infrared spectroscopy procedures for prediction of cassava root dry matter and amylose contents in Ugandan cassava germplasm. Journal of the Science of Food and Agriculture, 1-18.
dc.identifier.issn0022-5142
dc.identifier.urihttps://hdl.handle.net/20.500.12478/8294
dc.description.abstractBackground Cassava utilization for food and/or industrial products depends on inherent properties of root dry matter content (DMC) and the starch fraction of amylose content (AC). Accordingly, in this study, NIRS models were developed to aid breeding and selection of DMC and AC as critical industrial traits taking care of root sample preparation and cassava germplasm diversity available in Uganda. Results Upon undertaking calibrations and cross-validations, best models were adopted for validation. DMC in calibration samples ranged from 20 to 45g kg^-1 while for amylose content it ranged from 14 to 33g kg^-1. In the validation set average DMC was 29.5g kg^-1 while for the amylose content it was 24.64g kg^-1. For DMC, Modified Partial least square (MPLS) regression model had regression coefficients (R2) of 0.98 and 0.96 respectively, in the calibration and validation set. These were also associated with low bias (-0.018) and ratio of performance deviation that ranged from 4.7 to 5.0. In addition, standard error of prediction values ranged from 0.9g kg^-1 to 1.06g kg^-1. For AC, the regression coefficient was 0.91 for the calibration set and 0.94 for the validation set. A bias equivalent to -0.03 and ratio of performance deviation of 4.23 were observed. Conclusions These findings confirm the robustness of NIRS in estimation of dry matter content and amylose content in cassava roots and thus justify its use in routine cassava breeding operations.
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.format.extent1-18
dc.language.isoen
dc.subjectCassava
dc.subjectManihot Esculenta
dc.subjectDry Matter Content
dc.subjectAmylose
dc.subjectInfrared Spectrophotometry
dc.subjectSelection
dc.titleDevelopment and validation of near-infrared spectroscopy procedures for prediction of cassava root dry matter and amylose contents in Ugandan cassava germplasm
dc.typeJournal Article
cg.contributor.crpAgriculture for Nutrition and Health
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.affiliationNational Crops Resources Research Institute, Uganda
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationMakerere University
cg.contributor.affiliationNational Semi-Arid Resources Research Institute
cg.contributor.affiliationUniversity of Montpellier
cg.coverage.regionAfrica
cg.coverage.regionEast Africa
cg.coverage.countryUganda
cg.coverage.hubSouthern Africa Hub
cg.researchthemeNutrition and Human Health
cg.identifier.bibtexciteidNUWAMANYA:2023
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectCassava
cg.iitasubjectFood Security
cg.iitasubjectLivelihoods
cg.iitasubjectNutrition
cg.iitasubjectPost-Harvesting Technology
cg.journalJournal of the Science of Food and Agriculture
cg.notesPublished online: 04 Sep 2023
cg.accessibilitystatusLimited Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCopyrighted; all rights reserved
cg.targetaudienceScientists
cg.identifier.doihttps://doi.org/10.1002/jsfa.12966
cg.iitaauthor.identifierAlamu Emmanuel Oladeji: 0000-0001-6263-1359
cg.futureupdate.requiredNo


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