Machine learning methods in near infrared spectroscopy for predicting sensory traits in sweetpotatoes

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cg.authorship.typesCGIAR and developing country instituteen
cg.contributor.affiliationInternational Potato Centeren
cg.contributor.donorBill & Melinda Gates Foundationen
cg.coverage.countryUganda
cg.coverage.iso3166-alpha2UG
cg.coverage.regionAfrica
cg.creator.identifierGabriela Burgos Zapata: 0000-0002-0268-6785en
cg.creator.identifierMariam Nakitto: 0000-0002-4140-7216en
cg.creator.identifierfabrice davrieux: 0000-0002-7490-3611en
cg.creator.identifierReuben SSALI Tendo: 0000-0002-8143-6564en
cg.howPublishedFormally Publisheden
cg.identifier.doihttps://doi.org/10.1016/j.saa.2024.124406en
cg.isijournalISI Journalen
cg.issn1873-3557en
cg.journalSpectrochimica Acta Part A: Molecular and Biomolecular Spectroscopyen
cg.reviewStatusPeer Reviewen
cg.subject.actionAreaGenetic Innovation
cg.subject.actionAreaResilient Agrifood Systems
cg.subject.cipBREEDINGen
cg.subject.cipFOOD SECURITYen
cg.subject.cipFOOD SYSTEMSen
cg.subject.cipSWEET POTATOESen
cg.subject.cipNUTRITIONen
cg.subject.cipCROP IMPROVEMENTen
cg.subject.impactAreaNutrition, health and food security
cg.subject.impactAreaClimate adaptation and mitigation
cg.subject.sdgSDG 2 - Zero hungeren
cg.subject.sdgSDG 12 - Responsible consumption and productionen
cg.subject.sdgSDG 13 - Climate actionen
cg.subject.sdgSDG 9 - Industry, innovation and infrastructureen
cg.volume318en
dc.contributor.authorNantongo, J.S.en
dc.contributor.authorSerunkuma, E.en
dc.contributor.authorBurgos, C.en
dc.contributor.authorNakitto, M.en
dc.contributor.authorDavrieux, F.en
dc.contributor.authorSsali, R.T.en
dc.date.accessioned2024-05-06T19:51:35Zen
dc.date.available2024-05-06T19:51:35Zen
dc.identifier.urihttps://hdl.handle.net/10568/141737
dc.titleMachine learning methods in near infrared spectroscopy for predicting sensory traits in sweetpotatoesen
dcterms.abstractIt has been established that near infrared (NIR) spectroscopy has the potential of estimating sensory traits given the direct spectral responses that these properties have in the near infrared (NIR) region. In sweetpotato, sensory traits are key for improving acceptability of the crop for food security and nutrition. Studies have statistically modelled the levels of near infrared (NIR) spectroscopy sensory characteristics using Partial Least Squares (PLS) regression methods. To improve prediction accuracy, there are many advanced modelling techniques, particularly, which could be helpful when handling fresh (wet and un-processed) samples or where modelling may involve nonlinear dependence relationships. Performance of different quantitative prediction models for sensory traits developed using different machine learning methods were compared. Overall, results show that linear methods; linear support vector machine (L-SVM), principal component regression (PCR) and PLS performed better than other statistical methods. For all the 27 sensory traits, calibration models using L-SVM and PCR has slightly higher overall R2 (x = 0.33) compared to PLS (x̄ ̅= 0.32) and radial based SVM (NL-SVM; x ̅= 0.30). The levels of orange color intensity were the best predicted by all the calibration models (R2 = 0.87 – 0.89). The elastic net linear regression (ENR) and tree-based methods; extreme gradient boost (XGBoost) and random forest (RF) performed worse than would be expected but could possibly be improved with increased sample size. Lower average R2 were observed for calibration models of ENR (x ̅ = 0.26), XGBOOST (x ̅ = 0.26) and RF (x ̅ = 0.22). The overall RMSE in calibration, models was lower in PCR models (X = 0.82) compared to L-SVM (x= 0.86) and PLS (x= 0.90). ENR, XGboost and RF also had higher RMSE (0.90 -0.92). Effective wavelengths selection using the interval partial least-squares regression (iPLS), improved the robustness of the models but did not perform as good as the PLS. SNV pre-treatment was useful in improving model robustness.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.available2024-04en
dcterms.bibliographicCitationNantongo, J.; Serunkuma, E.; Burgos, C.; Nakitto, M.; Davrieux, F.; Ssali, R. 2024. Machine learning methods in near infrared spectroscopy for predicting sensory traits in sweetpotatoes. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. ISSN 1873-3557. https://doi.org/10.1016/j.saa.2024.124406en
dcterms.issued2024-04en
dcterms.languageen
dcterms.licenseCopyrighted; all rights reserved
dcterms.publisherElsevieren
dcterms.subjectphenotypingen
dcterms.subjectbreedingen
dcterms.subjectconsumer behaviouren
dcterms.subjectfood securityen
dcterms.subjectsweet potatoesen
dcterms.subjectmachine learningen
dcterms.subjectcrop improvementen
dcterms.typeJournal Article

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