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PLS-R calibration models for wine spirit volatile phenols prediction by near-infrared spectroscopy

2021-12-31Journal article Open access
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datacite.subject.sdg09:Indústria, Inovação e Infraestruturaspt_PT
datacite.subject.sdg12:Produção e Consumo Sustentáveispt_PT
dc.contributor.authorAnjos, Ofélia
dc.contributor.authorCaldeira, Ilda
dc.contributor.authorFernandes, Tiago
dc.contributor.authorPedro, Soraia
dc.contributor.authorVitória, Cláudia
dc.contributor.authorAlves, Sheila Cristina Oliveira
dc.contributor.authorCatarino, Sofia
dc.contributor.authorCanas, Sara
dc.date.accessioned2023-01-04T11:18:32Z
dc.date.available2023-01-04T11:18:32Z
dc.date.issued2021-12-31
dc.description.abstractNear-infrared spectroscopic (NIR) technique was used, for the first time, to predict volatile phenols content, namely guaiacol, 4-methyl-guaiacol, eugenol, syringol, 4-methyl-syringol and 4-allyl-syringol, of aged wine spirits (AWS). This study aimed to develop calibration models for the volatile phenol’s quantification in AWS, by NIR, faster and without sample preparation. Partial least square regression (PLS-R) models were developed with NIR spectra in the near-IR region (12,500–4000 cm−1) and those obtained from GC-FID quantification after liquid-liquid extraction. In the PLS-R developed method, cross-validation with 50% of the samples along a validation test set with 50% of the remaining samples. The final calibration was performed with 100% of the data. PLS-R models with a good accuracy were obtained for guaiacol (r2 = 96.34; RPD = 5.23), 4-methyl-guaiacol (r2 = 96.1; RPD = 5.07), eugenol (r2 = 96.06; RPD = 5.04), syringol (r2 = 97.32; RPD = 6.11), 4-methyl-syringol (r2 = 95.79; RPD = 4.88) and 4-allyl-syringol (r2 = 95.97; RPD = 4.98). These results reveal that NIR is a valuable technique for the quality control of wine spirits and to predict the volatile phenols content, which contributes to the sensory quality of the spirit beverages.pt_PT
dc.description.sponsorshipThe authors thank “Centro de Biotecnologia de plantas” for the equipment availability and Vitor de Freitas as the Scientific Consultant of the Project PO-CI-01-0145-FEDER-027819. This research was funded by National Funds through FCT—Foundation for Science and Technology under the Project POCI-01-0145-FEDER-027819 (PTDC/OCE-ETA/27819/2017). This work is also funded by National Funds through FCT—Foundation for Science and Technology under the Projects UIDB/00239/2020 [CEF], UIDB/05183/2020 [MED]; UIDB/00100/2020, UIDP/00100/2020 [CQE]; UID/AGR/04129/2020, DL 57/2016/CP1382/CT0025 [LEAF].pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/s22010286pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.2/12975
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationForest Research Centre
dc.relationMediterranean Institute for Agriculture, Environment and Development
dc.relationCentro de Química Estrutural
dc.relationCentro de Química Estrutural
dc.relationNot Available
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/22/1/286pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectNIRpt_PT
dc.subjectCalibration modelspt_PT
dc.subjectPLS-Rpt_PT
dc.subjectVolatile phenolspt_PT
dc.subjectAged wine spiritpt_PT
dc.titlePLS-R calibration models for wine spirit volatile phenols prediction by near-infrared spectroscopypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleForest Research Centre
oaire.awardTitleMediterranean Institute for Agriculture, Environment and Development
oaire.awardTitleCentro de Química Estrutural
oaire.awardTitleCentro de Química Estrutural
oaire.awardTitleNot Available
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00239%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05183%2F2020/PT
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oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00100%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/DL 57%2F2016/DL 57%2F2016%2FCP1382%2FCT0025/PT
oaire.citation.issue1pt_PT
oaire.citation.startPage286pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume22pt_PT
oaire.fundingStream6817 - DCRRNI ID
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oaire.fundingStreamDL 57/2016
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