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dc.contributor.authorFengou, Lemonia Christina
dc.contributor.authorMporas, Iosif
dc.contributor.authorSpyrelli, Evgenia
dc.contributor.authorLianou, Alexandra
dc.contributor.authorNychas, George-John
dc.date.accessioned2020-06-11T00:08:57Z
dc.date.available2020-06-11T00:08:57Z
dc.date.issued2020-06-08
dc.identifier.citationFengou , L C , Mporas , I , Spyrelli , E , Lianou , A & Nychas , G-J 2020 , ' Estimation of the Microbiological Quality of Meat using Rapid and Non-Invasive Spectroscopic Sensors ' , IEEE Access . https://doi.org/10.1109/ACCESS.2020.3000690
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/2299/22841
dc.description© 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
dc.description.abstractSpectroscopic methods in tandem with machine learning methodologies have attracted considerable research interest for the estimation of food quality. The objective of this study was the evaluation of Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) coupled with appropriate machine learning regression algorithms for assessing meat microbiological quality. For this purpose, minced pork patties were stored aerobically and under modified atmosphere packaging (MAP) conditions, at isothermal and dynamic temperature conditions. At regular time intervals during storage, samples were subjected to (i) microbiological analysis, (ii) FTIR measurements and (iii) MSI acquisition. The collected FTIR data were processed by feature extraction methods to reduce dimensionality, and subsequently Support Vector Machines (SVM) regression models were trained using spectral features (FTIR and MSI) to estimate microbiological quality of meat (microbial population). The regression models were evaluated with different experimental replicates using distinct meat batches. The performance of the models was evaluated in terms of correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE) and residual prediction deviation (RPD). The RMSE values for the microbial population estimation models using FTIR were 1.268 and 1.024 for aerobic and MAP storage, respectively. The performance in terms of RMSE for the MSI-based models was 1.144 for aerobic and 0.923 for MAP storage, while the combination of FTIR and MSI spectra resulted in models with RMSE equal to 1.146 for aerobic and 0.886 for MAP storage. The experimental results demonstrated the potential of estimating the microbiological quality of minced pork meat from spectroscopic data.en
dc.format.extent1531311
dc.language.isoeng
dc.relation.ispartofIEEE Access
dc.titleEstimation of the Microbiological Quality of Meat using Rapid and Non-Invasive Spectroscopic Sensorsen
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionBioEngineering
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.description.statusPeer reviewed
rioxxterms.versionofrecord10.1109/ACCESS.2020.3000690
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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