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Convolutional neural network allows amylose content prediction in yam (Dioscorea alata L.) flour using near infrared spectroscopy

dc.contributor.authorHoungbo, M.E.
dc.contributor.authorDesfontaines, L.
dc.contributor.authorDiman, J.L.
dc.contributor.authorArnau, G.
dc.contributor.authorMestres, C.
dc.contributor.authorDavrieux, F.
dc.contributor.authorRouan, L.
dc.contributor.authorBeurier, G.
dc.contributor.authorMarie-Magdeleine, C.
dc.contributor.authorMeghar, K.
dc.contributor.authorAlamu, E.O.
dc.contributor.authorOtegbayo, B.
dc.contributor.authorCornet, D.
dc.date.accessioned2023-09-05T09:47:40Z
dc.date.available2023-09-05T09:47:40Z
dc.date.issued2023-07-03
dc.identifier.citationHoungbo, M.E., Desfontaines, L., Diman, J.L., Arnau, G., Mestres, C., Davrieux, F., ... & Cornet, D. (2023). Convolutional neural network allows amylose content prediction in yam (Dioscorea alata L.) flour using near infrared spectroscopy. Journal of the Science of Food and Agriculture, 1-15.
dc.identifier.issn0022-5142
dc.identifier.urihttps://hdl.handle.net/20.500.12478/8244
dc.description.abstractBackground: Yam (Dioscorea alata L.) is the staple food of many populations in the intertropical zone where it is grown. The lack of phenotyping methods for tuber quality hinders the adoption of new genotypes from the breeding programs. Recently, near infrared spectroscopy (NIRS) has been used as a reliable tool to characterize the chemical composition of the yam tuber. However, it failed to predict the amylose content, although this trait is strongly involved in the quality of the product. Results: This study used NIRS to predict the amylose content from 186 yam flour samples. Two calibration methods were developed and validated on an independent dataset: Partial Least Square (PLS) and Convolutional Neural Network (CNN). To evaluate final model performances, the coefficient of determination (R2 ), the root mean square error (RMSE), and the Ratio of Performance to Deviation (RPD) were calculated using predictions on an independent validation dataset. Tested models showed contrasting performances (i.e. R2 of 0.72 and 0.89, RMSE of 1.33 and 0.81, RPD of 2.13 and 3.49 respectively, for the PLS and the CNN model). Conclusion: According to the quality standard for NIRS model prediction used in food science, the PLS method proved unsuccessful (RPD<3 and R2 <0.8) for predicting amylose content from yam flour, while the CNN proved reliable and efficient method. With the application of deep learning method, this study established the proof of concept that amylose content, a key driver of yam textural quality and acceptance, could be predicted accurately using NIRS as a high throughput phenotyping method. This article is protected by copyright. All rights reserved.
dc.description.sponsorshipFrench Agricultural Research Centre for International Development
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.format.extent1-15
dc.language.isoen
dc.subjectAmylose
dc.subjectConsumers
dc.subjectAcceptability
dc.subjectPhenotypes
dc.subjectInfrared Spectrophotometry
dc.subjectYams
dc.subjectFood Production
dc.subjectYields
dc.titleConvolutional neural network allows amylose content prediction in yam (Dioscorea alata L.) flour using near infrared spectroscopy
dc.typeJournal Article
cg.contributor.crpAgriculture for Nutrition and Health
cg.contributor.affiliationCentre de Coopération Internationale en Recherche Agronomique pour le Développement
cg.contributor.affiliationUniversite de Montpellier
cg.contributor.affiliationCentre de Recherche Antilles-Guyane, France
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationBowen University
cg.coverage.regionACP
cg.coverage.regionEurope
cg.coverage.countryFrance
cg.coverage.hubSouthern Africa Hub
cg.researchthemeNutrition and Human Health
cg.identifier.bibtexciteidHOUNGBO:2023
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectFood Security
cg.iitasubjectLivelihoods
cg.iitasubjectNutrition
cg.iitasubjectPost-Harvesting Technology
cg.iitasubjectValue Chains
cg.iitasubjectYam
cg.journalJournal of the Science of Food and Agriculture
cg.notesPublished online: 03 Jul 2023
cg.accessibilitystatusLimited Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCopyrighted; all rights reserved
cg.targetaudienceScientists
cg.identifier.doihttps://doi.org/10.1002/jsfa.12825
cg.iitaauthor.identifierAlamu Emmanuel Oladeji: 0000-0001-6263-1359
cg.futureupdate.requiredNo


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