Publication:
Using metaheuristic algorithms to improve the estimation of acidity in Fuji apples using NIR spectroscopy

Simple item page
dc.contributor.authorPourdarbani, Razieh
dc.contributor.authorSabzi, Sajad
dc.contributor.authorRohban, Mohammad H.
dc.contributor.authorGarcía Mateos, Ginés
dc.contributor.authorPaliwal, Jitendra
dc.contributor.authorMolina‑Martínez, José Miguel
dc.contributor.departmentInformática y Sistemas
dc.contributor.otherFacultades de la UMU::Facultad de Informática
dc.date.accessioned2026-01-19T08:55:07Z
dc.date.available2026-01-19T08:55:07Z
dc.date.copyright© 2022 The Authors
dc.date.issued2022-11-01
dc.description.abstractThis study focuses on the spectrochemical estimation of pH and titratable acidity (TA) of apples of Fuji variety at different stages of ripening. A novel approach is proposed for near-infrared (NIR) spectral analysis using hybrid machine learning methods that combine artificial neural networks (ANN) and metaheuristic algorithms. One hundred twenty samples were collected at three ripening stages and spectral data within two bands of NIR were extracted from each sample to predict the acidity properties. Alternatively, the 4 most effective wavelengths were also selected using a hybrid of ANN and the cultural algorithm. The experimental results prove that the models using spectral bands and the 4 most effective wavelengths are comparable, with a correlation coefficient, R, of 0.926 for the prediction of pH and 0.925 for TA using spectral bands, while for the second approach the R obtained were 0.924 and 0.920 for pH and TA, respectively. The models could not accurately predict extremely high or low pH and TA values, due to the clusters that formed after regression. However, for a classification problem in low/high acidity, both approaches were able to achieve a high accuracy of 100% for pH and 99.2% for TA.
dc.formatapplication/pdf
dc.format.extent11
dc.identifier.citationAin Shams Engineering Journal, 2022, vol. 13, no 6, p. 101776.
dc.identifier.doihttps://doi.org/10.1016/j.asej.2022.101776
dc.identifier.eissn2090-4495
dc.identifier.issn2090-4479
dc.identifier.urihttp://hdl.handle.net/10201/188129
dc.languageeng
dc.publisherElsevier
dc.relationThis work was partly supported by the Spanish Ministerio de Ciencia, Innovación y Universidades (MCIU), Ministerio de Ciencia e Innovación (MICINN) and Agencia Estatal de Investigación (AEI), as well as European Commission FEDER funds, under Grant RTI2018-098156-B-C53 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”.
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S2090447922000879
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectNear infrared
dc.subjectSpectroscopy
dc.subjectHybrid ANN
dc.subjectNon destructive estimation
dc.subjectTitratable acidity
dc.subject.odsObjetivo 2: Hambre y seguridad alimentaria
dc.subject.odsObjetivo 12: Producción y consumo sostenibles
dc.titleUsing metaheuristic algorithms to improve the estimation of acidity in Fuji apples using NIR spectroscopy
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublicationes
relation.isAuthorOfPublicationbb68ac7e-f852-4bc6-a7b4-e9a37a5617c2
relation.isAuthorOfPublication.latestForDiscoverybb68ac7e-f852-4bc6-a7b4-e9a37a5617c2
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
1-s2.0-S2090447922000879-main.pdf
Size:
5.03 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.37 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Artículos