Visible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef Longissimus dorsi muscle under simulated normal and abuse storage conditions

[EN] There is a need to develop a rapid technique to provide real time information on the microbial load of meat along the supply chain. Hyperspectral imaging (HSI) is a rapid, non-destructive technique well suited to food analysis applications. In this study, HSI in both the visible and near infrar...

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Detalles Bibliográficos
Autores: Achata, Eva M., Sousa Oliveira, Marcia Patricia de, Esquerre, Carlos A., Tiwari, Brijesh K., O'Donnell, Colm P.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de León
Repositorio:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/21799
Acceso en línea:https://www.sciencedirect.com/science/article/pii/S0023643820304527?via%3Dihub
http://hdl.handle.net/10612/21799
Access Level:acceso abierto
Palabra clave:Tecnología de los alimentos
Hyperspectral imaging
Chemometrics
TVC prediction
Data fusion
Meat
3309.13 Conservación de Alimentos
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repository_id_str
spelling Visible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef Longissimus dorsi muscle under simulated normal and abuse storage conditionsAchata, Eva M.Sousa Oliveira, Marcia Patricia deEsquerre, Carlos A.Tiwari, Brijesh K.O'Donnell, Colm P.Tecnología de los alimentosHyperspectral imagingChemometricsTVC predictionData fusionMeat3309.13 Conservación de Alimentos[EN] There is a need to develop a rapid technique to provide real time information on the microbial load of meat along the supply chain. Hyperspectral imaging (HSI) is a rapid, non-destructive technique well suited to food analysis applications. In this study, HSI in both the visible and near infrared spectral ranges, and chemometrics were studied for prediction of the bacterial growth on beef Longissimus dorsi muscle (LD) under simulated normal (4 °C) and abuse (10 °C) storage conditions. Total viable count (TVC) prediction models were developed using partial least squares regression (PLS-R), spectral pre-treatments, band selection and data fusion methods. The best TVC prediction models developed for storage at 4 (RMSEp 0.58 log CFU/g, RPDp 4.13, R2 p 0.96), 10 °C (RMSEp 0.97 log CFU/g, RPDp 3.28, R2 p 0.94) or at either 4 or 10 °C (RMSEp 0.89 log CFU/g, RPDp 2.27, R2 p 0.86) were developed using high-level data fusion of both spectral regions. The use of appropriate spectral pre-treatments and band selection methods was key for robust model development. This study demonstrated the potential of HSI and chemometrics for real time monitoring to predict microbial growth on LD along the meat supply chainSIThe authors acknowledge funding for this project from FIRM (13/ FM/508) as administered by the Irish Department of Agriculture, Food & the MarineElsevierTecnologia de los AlimentosFacultad de Veterinaria2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://www.sciencedirect.com/science/article/pii/S0023643820304527?via%3Dihubhttp://hdl.handle.net/10612/21799reponame:BULERIA. Repositorio Institucional de la Universidad de Leóninstname:Universidad de LeónIngléshttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:buleria.unileon.es:10612/217992026-06-24T12:43:27Z
dc.title.none.fl_str_mv Visible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef Longissimus dorsi muscle under simulated normal and abuse storage conditions
title Visible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef Longissimus dorsi muscle under simulated normal and abuse storage conditions
spellingShingle Visible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef Longissimus dorsi muscle under simulated normal and abuse storage conditions
Achata, Eva M.
Tecnología de los alimentos
Hyperspectral imaging
Chemometrics
TVC prediction
Data fusion
Meat
3309.13 Conservación de Alimentos
title_short Visible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef Longissimus dorsi muscle under simulated normal and abuse storage conditions
title_full Visible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef Longissimus dorsi muscle under simulated normal and abuse storage conditions
title_fullStr Visible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef Longissimus dorsi muscle under simulated normal and abuse storage conditions
title_full_unstemmed Visible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef Longissimus dorsi muscle under simulated normal and abuse storage conditions
title_sort Visible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef Longissimus dorsi muscle under simulated normal and abuse storage conditions
dc.creator.none.fl_str_mv Achata, Eva M.
Sousa Oliveira, Marcia Patricia de
Esquerre, Carlos A.
Tiwari, Brijesh K.
O'Donnell, Colm P.
author Achata, Eva M.
author_facet Achata, Eva M.
Sousa Oliveira, Marcia Patricia de
Esquerre, Carlos A.
Tiwari, Brijesh K.
O'Donnell, Colm P.
author_role author
author2 Sousa Oliveira, Marcia Patricia de
Esquerre, Carlos A.
Tiwari, Brijesh K.
O'Donnell, Colm P.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Tecnologia de los Alimentos
Facultad de Veterinaria
dc.subject.none.fl_str_mv Tecnología de los alimentos
Hyperspectral imaging
Chemometrics
TVC prediction
Data fusion
Meat
3309.13 Conservación de Alimentos
topic Tecnología de los alimentos
Hyperspectral imaging
Chemometrics
TVC prediction
Data fusion
Meat
3309.13 Conservación de Alimentos
description [EN] There is a need to develop a rapid technique to provide real time information on the microbial load of meat along the supply chain. Hyperspectral imaging (HSI) is a rapid, non-destructive technique well suited to food analysis applications. In this study, HSI in both the visible and near infrared spectral ranges, and chemometrics were studied for prediction of the bacterial growth on beef Longissimus dorsi muscle (LD) under simulated normal (4 °C) and abuse (10 °C) storage conditions. Total viable count (TVC) prediction models were developed using partial least squares regression (PLS-R), spectral pre-treatments, band selection and data fusion methods. The best TVC prediction models developed for storage at 4 (RMSEp 0.58 log CFU/g, RPDp 4.13, R2 p 0.96), 10 °C (RMSEp 0.97 log CFU/g, RPDp 3.28, R2 p 0.94) or at either 4 or 10 °C (RMSEp 0.89 log CFU/g, RPDp 2.27, R2 p 0.86) were developed using high-level data fusion of both spectral regions. The use of appropriate spectral pre-treatments and band selection methods was key for robust model development. This study demonstrated the potential of HSI and chemometrics for real time monitoring to predict microbial growth on LD along the meat supply chain
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S0023643820304527?via%3Dihub
http://hdl.handle.net/10612/21799
url https://www.sciencedirect.com/science/article/pii/S0023643820304527?via%3Dihub
http://hdl.handle.net/10612/21799
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:BULERIA. Repositorio Institucional de la Universidad de León
instname:Universidad de León
instname_str Universidad de León
reponame_str BULERIA. Repositorio Institucional de la Universidad de León
collection BULERIA. Repositorio Institucional de la Universidad de León
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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