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dc.contributor.authorAchata, Eva M.
dc.contributor.authorOliveira, Marcia
dc.contributor.authorEsquerre, Carlos A.
dc.contributor.authorTiwari, Brijesh K
dc.contributor.authorO'Donnell, Colm P.
dc.date.accessioned2021-06-16T11:20:41Z
dc.date.available2021-06-16T11:20:41Z
dc.date.issued2020-06
dc.identifier.citationE. M. Achata, M. Oliveira, C. A. Esquerre, B. K. Tiwari, C. P. O'Donnell, Visible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef Longissimus dorsi muscle under simulated normal and abuse storage conditions, LWT, Volume 128, 2020, https://doi.org/10.1016/j.lwt.2020.109463en_US
dc.identifier.urihttp://hdl.handle.net/11019/2450
dc.descriptionpeer-revieweden_US
dc.description.abstractThere 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, R2p 0.96), 10 °C (RMSEp 0.97 log CFU/g, RPDp 3.28, R2p 0.94) or at either 4 or 10 °C (RMSEp 0.89 log CFU/g, RPDp 2.27, R2p 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.en_US
dc.description.sponsorshipFood Institutional Research Measure
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.relation.ispartofseriesLWT;128
dc.rights© 2020 Elsevier Ltd. All rights reserved.
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttps://www.elsevier.com/tdm/userlicense/1.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectHyperspectral imagingen_US
dc.subjectChemometricsen_US
dc.subjectTVC predictionen_US
dc.subjectData fusionen_US
dc.subjectMeaten_US
dc.titleVisible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef Longissimus dorsi muscle under simulated normal and abuse storage conditionsen_US
dc.typeArticleen_US
dc.embargo.terms24/04/2021en_US
dc.identifier.doihttps://doi.org/10.1016/j.lwt.2020.109463
dc.contributor.sponsorIrish Department of Agriculture, Food & the Marineen_US
dc.contributor.sponsorGrantNumber13/FM/508en_US
dc.source.volume128
dc.source.beginpage109463
refterms.dateFOA2021-04-24T00:00:00Z
dc.source.journaltitleLWT


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