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Identification of almond’s variety based on FTIR spectra of ground samples

2024Conference object Open access
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datacite.subject.fosCiências Agrárias::Biotecnologia Agrária e Alimentar
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg02:Erradicar a Fome
dc.contributor.authorLamas, Sandra
dc.contributor.authorRodrigues, Nuno
dc.contributor.authorSantamaria-Echart, Arantzazu
dc.contributor.authorPalu, Igor
dc.contributor.authorManchique, Jocyla R.
dc.contributor.authorLopéz-Cortés, Isabel
dc.contributor.authorPereira, José Alberto
dc.contributor.authorPeres, António M.
dc.date.accessioned2025-10-23T15:32:33Z
dc.date.available2025-10-23T15:32:33Z
dc.date.issued2024
dc.description.abstractAttenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) was employed to develop multivariate discriminant models for almond cultivar identification. For that, raw transmittance spectra were recorded for ground almonds in the range from 4000 to 500 cm-1 (Figure 1a). As can be inferred from the figure, The ATR-FTIR analysis unveiled distinct transmittance band spectral profiles, particularly emphasizing bands within the wavenumber ranges of 3700-2750 cm-1 and 1800-600 cm-1. Remarkably, no visible peaks were observed in the wavenumber ranges of 4000-3700 cm-1, 2750-1800 cm-1 and 600-500 cm-1, which are considered neutral spectral regions. The spectra analysis of the ground almond samples exhibited characteristic bands, namely at 3290, 2975, 2925, 2855, 1640, 1380, 1265, 1235, 1095, 1050, 995 and 905 cm-1. An accurate multivariate linear discriminant model (LDA) was established using transmittance data recorded at 30 wavenumbers, selected by applying the simulated annealing algorithm, which is a meta-heuristic variable selection algorithm. The model successfully discriminated the seven almond cultivars with correct classifications of 100%, 99.2% and 98.9% for training (Figure 1b), leave-one-out cross-validation and repeated K-fold cross-validation (10 repeats and 4 folds, which ensured keeping at least 25% of the initial dataset for cross-validation purposes). The predictive performances achieved in the present study are in line with those previously reported in the literature but for a smaller number of cultivars. Indeed, García et al. described the successful discrimination (LDA coupled with a stepwise variable selection algorithm: 100% of correct classifications for training) of three almond cultivars (one American: cv. Butte; and two Spanish: cvs. Marcona and Guara). Also, Cortés et al., applied the FTIR technique to differentiate four Spanish almond cultivars (cvs. Guara, Rumbeta, Marcona, and Planeta) based on absorbance spectra, being to accurately predict (external dataset) the almond cultivar with a success rate of 94.45% using a Partial Least Square Regression-Discriminant Analysis model. The same research team demonstrated the successful application of the FTIR technique in discriminating whole almonds based on their bitterness levels (sweet almonds versus bitter almonds). In conclusion, the proposed a FTIR-LDA-SA classification approach based on raw transmittance spectra recorded for ground almonds was shown to be a rapid, cost-effective, and minimally invasive tool for almond cultivar traceability that can be of utmost relevance for producers and industrial stakeholders throughout the almond chain.eng
dc.description.sponsorshipThe authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support by national funds FCT/MCTES (PIDDAC) to CIMO (UIDB/00690/2020, https://doi.org/10.54499/UIDB/00690/2020; and UIDP/00690/2020, https://doi.org/10.54499/UIDP/00690/2020) unit as well as to the Associate Laboratory SusTEC (LA/P/0007/2020). National funding by FCT- Foundation for Science and Technology, through the institutional scientific employment program-contract with Nuno Rodrigues. Sandra Lamas acknowledges the Ph.D. research grant (2022.10070.BD) provided by FCT.
dc.identifier.citationLamas, Sandra; Rodrigues, Nuno; Santamaria-Echart, Arantzazu; Palu, Igor; Manchique, Jocyla R.; Lopéz-Cortés, Isabel; Pereira, José Alberto; Peres, António M. (2024). Identification of almond’s variety based on FTIR spectra of ground samples. In XVII Encontro Nacional Química dos Alimentos. Vila Real. ISBN 978-989-8124-45-6
dc.identifier.isbn978-989-8124-45-6
dc.identifier.urihttp://hdl.handle.net/10198/34872
dc.language.isoeng
dc.peerreviewedyes
dc.publisherUniversidade de Trás-os-Montes e Alto Douro
dc.relationMountain Research Center
dc.relationLA/P/0007/2020
dc.relation2022.10070.BD
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAlmond
dc.titleIdentification of almond’s variety based on FTIR spectra of ground samplespor
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleMountain Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00690%2F2020/PT
oaire.citation.conferenceDate2024
oaire.citation.conferencePlaceVila Real
oaire.citation.endPage258
oaire.citation.startPage258
oaire.citation.titleXVII Encontro Nacional Química dos Alimentos
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameLamas
person.familyNameRodrigues
person.familyNameSantamaria-Echart
person.familyNamePereira
person.familyNamePeres
person.givenNameSandra
person.givenNameNuno
person.givenNameArantzazu
person.givenNameJosé Alberto
person.givenNameAntónio M.
person.identifier107333
person.identifier.ciencia-id311F-FE90-417F
person.identifier.ciencia-idF41D-B424-5F78
person.identifier.ciencia-id0C17-1E1F-2621
person.identifier.ciencia-id611F-80B2-A7C1
person.identifier.ciencia-idCF16-5443-F420
person.identifier.orcid0000-0002-4334-1636
person.identifier.orcid0000-0002-9305-0976
person.identifier.orcid0000-0003-0107-7301
person.identifier.orcid0000-0002-2260-0600
person.identifier.orcid0000-0001-6595-9165
person.identifier.ridL-6798-2014
person.identifier.ridI-8470-2012
person.identifier.scopus-author-id57225883755
person.identifier.scopus-author-id55258560600
person.identifier.scopus-author-id56725697700
person.identifier.scopus-author-id57204366348
person.identifier.scopus-author-id7102331969
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
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