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Use of low cost near‑infrared spectroscopy, to predict pasting properties of high quality cassava flour

dc.contributor.authorAbubakar, M.
dc.contributor.authorWasswa, P.
dc.contributor.authorMasumba, E.
dc.contributor.authorOngom, P.
dc.contributor.authorMkamilo, G.
dc.contributor.authorKanju, E.
dc.contributor.authorAbincha, W.
dc.contributor.authorEdema, R.
dc.contributor.authorSichalwe, K.
dc.contributor.authorTukamuhabwa, P.
dc.contributor.authorKayondo, S.
dc.contributor.authorRabbi, I.
dc.contributor.authorKulembeka, H.
dc.date.accessioned2024-10-09T09:19:45Z
dc.date.available2024-10-09T09:19:45Z
dc.date.issued2024-07-25
dc.identifier.citationAbubakar, M., Wasswa, P., Masumba, E., Ongom, P., Mkamilo, G., Kanju, E., ... & Kulembeka, H. (2024). Use of low cost near-infrared spectroscopy, to predict pasting properties of high quality cassava flour. Scientific Reports, 14(1): 17130, 1-8.
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/20.500.12478/8590
dc.description.abstractDetermination of pasting properties of high quality cassava flour using rapid visco analyzer is expensive and time consuming. The use of mobile near infrared spectroscopy (SCiO™) is an alternative high throughput phenotyping technology for predicting pasting properties of high quality cassava flour traits. However, model development and validation are necessary to verify that reasonable expectations are established for the accuracy of a prediction model. In the context of an ongoing breeding effort, we investigated the use of an inexpensive, portable spectrometer that only records a portion (740–1070 nm) of the whole NIR spectrum to predict cassava pasting properties. Three machine-learning models, namely glmnet, lm, and gbm, implemented in the Caret package in R statistical program, were solely evaluated. Based on calibration statistics (R2, RMSE and MAE), we found that model calibrations using glmnet provided the best model for breakdown viscosity, peak viscosity and pasting temperature. The glmnet model using the first derivative, peak viscosity had calibration and validation accuracy of R2 = 0.56 and R2 = 0.51 respectively while breakdown had calibration and validation accuracy of R2 = 0.66 and R2 = 0.66 respectively. We also found out that stacking of pre-treatments with Moving Average, Savitzky Golay, First Derivative, Second derivative and Standard Normal variate using glmnet model resulted in calibration and validation accuracy of R2 = 0.65 and R2 = 0.64 respectively for pasting temperature. The developed calibration model predicted the pasting properties of HQCF with sufficient accuracy for screening purposes. Therefore, SCiO™ can be reliably deployed in screening early-generation breeding materials for pasting properties.
dc.description.sponsorshipUK’s Foreign, Commonwealth & Development Office
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.format.extent1-8
dc.language.isoen
dc.subjectTemperature
dc.subjectViscosity
dc.subjectForecasting
dc.subjectPhenotypes
dc.subjectCassava
dc.subjectCalibration
dc.subjectTanzania
dc.titleUse of low cost near‑infrared spectroscopy, to predict pasting properties of high quality cassava flour
dc.typeJournal Article
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.affiliationMakerere University
cg.contributor.affiliationTanzania Agricultural Research Institute
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationKenya Agricultural and Livestock Research Organization
cg.coverage.regionAfrica
cg.coverage.regionEast Africa
cg.coverage.countryTanzania
cg.coverage.hubEastern Africa Hub
cg.coverage.hubHeadquarters and Western Africa Hub
cg.researchthemeBiotech and Plant Breeding
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectAgronomy
cg.iitasubjectCassava
cg.iitasubjectFood Security
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Production
cg.journalScientific Reports
cg.notesOpen Access Journal
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution 4.0 (CC BY 0.0)
cg.targetaudienceScientists
cg.identifier.doihttps://doi.org/10.1038/s41598-024-67299-w
cg.iitaauthor.identifierPatrick Ongom: 0000-0002-5303-3602
cg.iitaauthor.identifierEdward Kanju: 0000-0002-0413-1302
cg.iitaauthor.identifierKayondo Siraj Ismail: 0000-0002-3212-5727
cg.iitaauthor.identifierIsmail Rabbi: 0000-0001-9966-2941
cg.futureupdate.requiredNo
cg.identifier.issue1: 17130
cg.identifier.volume14


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