PLS-R calibration models for wine spirit volatile phenols prediction by Near-Infrared Spectroscopy
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.11/7843 |
Resumo: | Near-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-methylsyringol (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. |
id |
RCAP_9f7e5e0c330792090274cd926921e02c |
---|---|
oai_identifier_str |
oai:repositorio.ipcb.pt:10400.11/7843 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
PLS-R calibration models for wine spirit volatile phenols prediction by Near-Infrared SpectroscopyNIRCalibration modelsPLS-RVolatile phenolsAged wine spiritNear-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-methylsyringol (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.Repositório Científico do Instituto Politécnico de Castelo BrancoAnjos, O.Caldeira, IldaFernandes, Tiago A.Pedro, SoraiaVitória, CláudiaAlves, Sheila OliveiraCatarino, SofiaCanas, Sara2022-01-12T12:33:35Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.11/7843engANJOS, O [et al.] (2021) - PLS-R calibration models for wine spirit volatile phenols prediction by Near-Infrared Spectroscopy. Sensors.Vol. 22, n.º 1, p. 286. DOI 10.3390/s2201028610.3390/s22010286info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-09-14T01:48:58Zoai:repositorio.ipcb.pt:10400.11/7843Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-09-14T01:48:58Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
PLS-R calibration models for wine spirit volatile phenols prediction by Near-Infrared Spectroscopy |
title |
PLS-R calibration models for wine spirit volatile phenols prediction by Near-Infrared Spectroscopy |
spellingShingle |
PLS-R calibration models for wine spirit volatile phenols prediction by Near-Infrared Spectroscopy Anjos, O. NIR Calibration models PLS-R Volatile phenols Aged wine spirit |
title_short |
PLS-R calibration models for wine spirit volatile phenols prediction by Near-Infrared Spectroscopy |
title_full |
PLS-R calibration models for wine spirit volatile phenols prediction by Near-Infrared Spectroscopy |
title_fullStr |
PLS-R calibration models for wine spirit volatile phenols prediction by Near-Infrared Spectroscopy |
title_full_unstemmed |
PLS-R calibration models for wine spirit volatile phenols prediction by Near-Infrared Spectroscopy |
title_sort |
PLS-R calibration models for wine spirit volatile phenols prediction by Near-Infrared Spectroscopy |
author |
Anjos, O. |
author_facet |
Anjos, O. Caldeira, Ilda Fernandes, Tiago A. Pedro, Soraia Vitória, Cláudia Alves, Sheila Oliveira Catarino, Sofia Canas, Sara |
author_role |
author |
author2 |
Caldeira, Ilda Fernandes, Tiago A. Pedro, Soraia Vitória, Cláudia Alves, Sheila Oliveira Catarino, Sofia Canas, Sara |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico de Castelo Branco |
dc.contributor.author.fl_str_mv |
Anjos, O. Caldeira, Ilda Fernandes, Tiago A. Pedro, Soraia Vitória, Cláudia Alves, Sheila Oliveira Catarino, Sofia Canas, Sara |
dc.subject.por.fl_str_mv |
NIR Calibration models PLS-R Volatile phenols Aged wine spirit |
topic |
NIR Calibration models PLS-R Volatile phenols Aged wine spirit |
description |
Near-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-methylsyringol (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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 2021-01-01T00:00:00Z 2022-01-12T12:33:35Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.11/7843 |
url |
http://hdl.handle.net/10400.11/7843 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
ANJOS, O [et al.] (2021) - PLS-R calibration models for wine spirit volatile phenols prediction by Near-Infrared Spectroscopy. Sensors.Vol. 22, n.º 1, p. 286. DOI 10.3390/s22010286 10.3390/s22010286 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
mluisa.alvim@gmail.com |
_version_ |
1817542981743280128 |