Feasibility study of near infrared spectroscopy to detect yellow stain on cork granulate

The aim of this study was to evaluate the viability of near infrared spectroscopy (NIRS) to detect the anomaly known as yellow stain on cork granulate. Detecting this anomaly is crucial to the cork granulate stopper industry, since it is associated with the presence of 2,4,6-Trichloroanisole (TCA),...

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Detalles Bibliográficos
Autores: Pérez Terrazas, David, González-Adrados, J. R., Sánchez-González, Mariola
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/290622
Acceso en línea:http://hdl.handle.net/10261/290622
Access Level:acceso abierto
Palabra clave:Cork
Granulate
Yellow Stain
2,4,6-Trichloroanisole
TCA
Near Infrared Spectroscopy
NIRS
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spelling Feasibility study of near infrared spectroscopy to detect yellow stain on cork granulatePérez Terrazas, DavidGonzález-Adrados, J. R.Sánchez-González, MariolaCorkGranulateYellow Stain2,4,6-TrichloroanisoleTCANear Infrared SpectroscopyNIRSThe aim of this study was to evaluate the viability of near infrared spectroscopy (NIRS) to detect the anomaly known as yellow stain on cork granulate. Detecting this anomaly is crucial to the cork granulate stopper industry, since it is associated with the presence of 2,4,6-Trichloroanisole (TCA), this compound having been identified as the main agent responsible for cork offflavours. Samples for the NIRS spectra were prepared by mixing in different proportions cork granulate with high visual quality and cork granulate with yellow stain, obtaining 120 samples with 8 different percentages of yellow stain (0, 5, 10, 15, 25, 35, 50 and 100%). Two spectra per sample were collected using a Bruker MPA spectrophotometer and the partial least squares (PLS) method was used to obtain numerous equations. The best equation was obtained by utilizing the standard normal variate (SNV) spectral preprocessing, making use of only one specific part of the near infrared spectral range: 9400- 4250 cm-1. This equation shows a coefficient of determination (R²) of 99.42%, a root mean square error of cross validation (RMSECV) of 2.34%, and a residual prediction deviation (RPD) of 13.10. The critical level and the limit of detection are 3.8% and 7.6%, respectively. The calculated receiver operating characteristic (ROC) curves show great discrimination capacity and the area under the ROC curve (AUC) is higher than 0.93 in any case. This study demonstrates that NIRS provides a viable technique for detecting yellow stain in cork granulate.Italian Society of Silviculture and Forest Ecology202320232018info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://hdl.handle.net/10261/290622reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglésinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2906222026-05-22T06:33:51Z
dc.title.none.fl_str_mv Feasibility study of near infrared spectroscopy to detect yellow stain on cork granulate
title Feasibility study of near infrared spectroscopy to detect yellow stain on cork granulate
spellingShingle Feasibility study of near infrared spectroscopy to detect yellow stain on cork granulate
Pérez Terrazas, David
Cork
Granulate
Yellow Stain
2,4,6-Trichloroanisole
TCA
Near Infrared Spectroscopy
NIRS
title_short Feasibility study of near infrared spectroscopy to detect yellow stain on cork granulate
title_full Feasibility study of near infrared spectroscopy to detect yellow stain on cork granulate
title_fullStr Feasibility study of near infrared spectroscopy to detect yellow stain on cork granulate
title_full_unstemmed Feasibility study of near infrared spectroscopy to detect yellow stain on cork granulate
title_sort Feasibility study of near infrared spectroscopy to detect yellow stain on cork granulate
dc.creator.none.fl_str_mv Pérez Terrazas, David
González-Adrados, J. R.
Sánchez-González, Mariola
author Pérez Terrazas, David
author_facet Pérez Terrazas, David
González-Adrados, J. R.
Sánchez-González, Mariola
author_role author
author2 González-Adrados, J. R.
Sánchez-González, Mariola
author2_role author
author
dc.subject.none.fl_str_mv Cork
Granulate
Yellow Stain
2,4,6-Trichloroanisole
TCA
Near Infrared Spectroscopy
NIRS
topic Cork
Granulate
Yellow Stain
2,4,6-Trichloroanisole
TCA
Near Infrared Spectroscopy
NIRS
description The aim of this study was to evaluate the viability of near infrared spectroscopy (NIRS) to detect the anomaly known as yellow stain on cork granulate. Detecting this anomaly is crucial to the cork granulate stopper industry, since it is associated with the presence of 2,4,6-Trichloroanisole (TCA), this compound having been identified as the main agent responsible for cork offflavours. Samples for the NIRS spectra were prepared by mixing in different proportions cork granulate with high visual quality and cork granulate with yellow stain, obtaining 120 samples with 8 different percentages of yellow stain (0, 5, 10, 15, 25, 35, 50 and 100%). Two spectra per sample were collected using a Bruker MPA spectrophotometer and the partial least squares (PLS) method was used to obtain numerous equations. The best equation was obtained by utilizing the standard normal variate (SNV) spectral preprocessing, making use of only one specific part of the near infrared spectral range: 9400- 4250 cm-1. This equation shows a coefficient of determination (R²) of 99.42%, a root mean square error of cross validation (RMSECV) of 2.34%, and a residual prediction deviation (RPD) of 13.10. The critical level and the limit of detection are 3.8% and 7.6%, respectively. The calculated receiver operating characteristic (ROC) curves show great discrimination capacity and the area under the ROC curve (AUC) is higher than 0.93 in any case. This study demonstrates that NIRS provides a viable technique for detecting yellow stain in cork granulate.
publishDate 2018
dc.date.none.fl_str_mv 2018
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/290622
url http://hdl.handle.net/10261/290622
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Italian Society of Silviculture and Forest Ecology
publisher.none.fl_str_mv Italian Society of Silviculture and Forest Ecology
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869420494050033664
score 15,811543

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