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Predicting starch content in cassava fresh roots using near-infrared spectroscopy

dc.contributor.authorMbanjo, E.
dc.contributor.authorHershberger, J.M.
dc.contributor.authorPeteti, P.
dc.contributor.authorAgbona, A.
dc.contributor.authorIkpan, A.
dc.contributor.authorOgunpaimo, K.
dc.contributor.authorKayondo, S.I.
dc.contributor.authorAbioye, R.S.
dc.contributor.authorNafiu, K.
dc.contributor.authorAlamu, E.O.
dc.contributor.authorAdesokan, M.
dc.contributor.authorMaziya-Dixon, B.
dc.contributor.authorParkes, E.
dc.contributor.authorKulakow, P.
dc.contributor.authorGore, M.A.
dc.contributor.authorEgesi, C.
dc.contributor.authorRabbi, I.Y.
dc.date.accessioned2022-12-13T15:29:18Z
dc.date.available2022-12-13T15:29:18Z
dc.date.issued2022
dc.identifier.citationMbanjo, E., Hershberger, J.M., Peteti, P., Agbona, A., Ikpan, A., Ogunpaimo, K., ... & Rabbi, I.Y. (2022). Predicting starch content in cassava fresh roots using near-infrared spectroscopy. Frontiers in Plant Science, 1-16.
dc.identifier.issn1664-462X
dc.identifier.urihttps://hdl.handle.net/20.500.12478/7972
dc.description.abstractThe cassava starch market is promising in sub-Saharan Africa and increasing rapidly due to the numerous uses of starch in food industries. More accurate, high-throughput, and cost-effective phenotyping approaches could hasten the development of cassava varieties with high starch content to meet the growing market demand. This study investigated the effectiveness of a pocket-sized SCiO™ molecular sensor (SCiO) (740−1070 nm) to predict starch content in freshly ground cassava roots. A set of 344 unique genotypes from 11 field trials were evaluated. The predictive ability of individual trials was compared using partial least squares regression (PLSR). The 11 trials were aggregated to capture more variability, and the performance of the combined data was evaluated using two additional algorithms, random forest (RF) and support vector machine (SVM). The effect of pretreatment on model performance was examined. The predictive ability of SCiO was compared to that of two commercially available near-infrared (NIR) spectrometers, the portable ASD QualitySpec® Trek (QST) (350−2500 nm) and the benchtop FOSS XDS Rapid Content™ Analyzer (BT) (400−2490 nm). The heritability of NIR spectra was investigated, and important spectral wavelengths were identified. Model performance varied across trials and was related to the amount of genetic diversity captured in the trial. Regardless of the chemometric approach, a satisfactory and consistent estimate of starch content was obtained across pretreatments with the SCiO (correlation between the predicted and the observed test set, (R2 P): 0.84−0.90; ratio of performance deviation (RPD): 2.49−3.11, ratio of performance to interquartile distance (RPIQ): 3.24−4.08, concordance correlation coefficient (CCC): 0.91−0.94). While PLSR and SVM showed comparable prediction abilities, the RF model yielded the lowest performance. The heritability of the 331 NIRS spectra varied across trials and spectral regions but was highest (H2 > 0.5) between 871−1070 nm in most trials. Important wavelengths corresponding to absorption bands associated with starch and water were identified from 815 to 980 nm. Despite its limited spectral range, SCiO provided satisfactory prediction, as did BT, whereas QST showed less optimal calibration models. The SCiO spectrometer may be a cost-effective solution for phenotyping the starch content of fresh roots in resource-limited cassava breeding programs.
dc.description.sponsorshipUKs Foreign, Commonwealth & Development Office
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.format.extent1-16
dc.language.isoen
dc.subjectCassava
dc.subjectStarch
dc.subjectSpectrophotometers
dc.subjectSpectra
dc.subjectHeritability
dc.titlePredicting starch content in cassava fresh roots using near-infrared spectroscopy
dc.typeJournal Article
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationClemson University
cg.contributor.affiliationTexas A&M University
cg.contributor.affiliationCornell University
cg.coverage.regionAfrica
cg.coverage.regionWest Africa
cg.coverage.countryNigeria
cg.coverage.hubHeadquarters and Western Africa Hub
cg.researchthemeBiometrics
cg.researchthemeBiotech and Plant Breeding
cg.researchthemeNutrition and Human Health
cg.identifier.bibtexciteidMBANJO:2022
cg.isijournalISI Journal
cg.authorship.typesCGIAR and advanced research institute
cg.iitasubjectAgronomy
cg.iitasubjectBiometrics
cg.iitasubjectCassava
cg.iitasubjectFood Security
cg.iitasubjectLivelihoods
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Production
cg.journalFrontiers in Plant Science
cg.notesOpen Access Article; Published online: 08 Nov 2022
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution 4.0 (CC BY 0.0)
cg.targetaudienceScientists
cg.identifier.doihttps://dx.doi.org/10.3389/fpls.2022.990250
cg.iitaauthor.identifierEdwige Gaby Nkouaya Mbanjo: 0000-0002-9982-1137
cg.iitaauthor.identifierPrasad Peteti: 0000-0002-6013-8947
cg.iitaauthor.identifierKayondo Siraj Ismail: 0000-0002-3212-5727
cg.iitaauthor.identifierAlamu Emmanuel Oladeji: 0000-0001-6263-1359
cg.iitaauthor.identifierBusie Maziya-Dixon: 0000-0003-2014-2201
cg.iitaauthor.identifierE J Parkes: 0000-0003-4063-1483
cg.iitaauthor.identifierPeter Kulakow: 0000-0002-7574-2645
cg.iitaauthor.identifierChiedozie Egesi: 0000-0002-9063-2727
cg.iitaauthor.identifierIsmail Rabbi: 0000-0001-9966-2941
cg.futureupdate.requiredNo


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