Publicação
Spectral markers and machine learning: Revolutionizing Rice evaluation with near infrared spectroscopy
| datacite.subject.fos | Ciências Naturais::Ciências Químicas | |
| datacite.subject.fos | Ciências Agrárias::Biotecnologia Agrária e Alimentar | |
| datacite.subject.fos | Ciências Naturais::Ciências Biológicas | |
| datacite.subject.sdg | 09:Indústria, Inovação e Infraestruturas | |
| dc.contributor.author | Sampaio, Pedro Sousa | |
| dc.contributor.author | Carbas, Bruna | |
| dc.contributor.author | Soares, Andreia | |
| dc.contributor.author | Sousa, Inês | |
| dc.contributor.author | Brites, Carla | |
| dc.date.accessioned | 2025-11-14T17:10:06Z | |
| dc.date.available | 2025-11-14T17:10:06Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | The evaluation of rice varieties is a complex, time-consuming process requiring advanced equipment. This study aimed to discriminate 22 commercial rice varieties from six types by analyzing biochemical, physicochemical, and cooking properties. Near-infrared (NIR) spectroscopy, combined with machine learning, linked molecular properties with quality traits, offering a high-throughput solution. Partial Least Squares (PLS) models accurately predicted parameters such as whiteness (R2 = 0.94), width (R2 = 0.94), resilience (R2 = 0.96), and springiness (R2 = 0.98), highlighting key wavelength regions. Principal Component Analysis (PCA) revealed distinct clustering patterns, while Partial Least Squares Discriminant Analysis (PLS-DA) achieved a 17 % error rate in external predictions. Spectral markers at A6032/4457 cm-1, A7004/5241 cm- 1, and A7004/4749 cm-1 reflected biomolecular differences among varieties. This innovative approach enables precise quantification, classification, and differentiation of rice types, enhancing quality control, improving consumer satisfaction, and optimizing breeding selection processes efficiently. | eng |
| dc.description.sponsorship | Funding for this research has been received from TRACE-RICE—Tracing rice and valorizing side streams along with Mediterranean blockchain, grant no. 1934 (call 2019, Section 1 Agrofood) of the PRIMA Program supported under Horizon 2020, the European Union's Framework Program for Research and Innovation, and Research Unit, GREEN-IT Bioresources for Sustainability Base Funding https://doi.org/10.54499/UIDB/04551/2020 | |
| dc.identifier.citation | Sampaio, Pedro Sousa; Carbas, Bruna; Soares, Andreia; Sousa, Inês; Brites, Carla (2025). Spectral markers and machine learning: Revolutionizing Rice evaluation with near infrared spectroscopy. Journal of Agricultural and Food Chemistry. ISSN 0308-8146. 492:3, p. 1-11 | |
| dc.identifier.doi | 10.1016/j.foodchem.2025.145569 | |
| dc.identifier.issn | 0308-8146 | |
| dc.identifier.uri | http://hdl.handle.net/10198/35080 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Elsevier | |
| dc.relation | GREEN-IT "Bioresources for Sustainability" | |
| dc.relation.ispartof | Food Chemistry | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | |
| dc.subject | Classification models | |
| dc.subject | Machine learning techniques | |
| dc.subject | NIR spectroscopy | |
| dc.subject | PCA | |
| dc.subject | PLS-DA | |
| dc.subject | Rice | |
| dc.subject | Spectral markers | |
| dc.title | Spectral markers and machine learning: Revolutionizing Rice evaluation with near infrared spectroscopy | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | GREEN-IT "Bioresources for Sustainability" | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04551%2F2020/PT | |
| oaire.citation.endPage | 11 | |
| oaire.citation.issue | 3 | |
| oaire.citation.startPage | 1 | |
| oaire.citation.title | Food Chemistry | |
| oaire.citation.volume | 492 | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Carbas | |
| person.givenName | Bruna | |
| person.identifier.ciencia-id | A41A-376D-3E8B | |
| person.identifier.orcid | 0000-0002-5941-8749 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| relation.isAuthorOfPublication | be27c391-b797-4aaf-851a-2c35c88a6b77 | |
| relation.isAuthorOfPublication.latestForDiscovery | be27c391-b797-4aaf-851a-2c35c88a6b77 | |
| relation.isProjectOfPublication | 68ac68c2-a4de-4a05-9295-fc48d29a11d0 | |
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