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Estimation of the Microbiological Quality of Meat using Rapid and Non-Invasive Spectroscopic Sensors

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Estimation of the Microbiological Quality of Meat using Rapid and Non-Invasive Spectroscopic Sensors. / Fengou, Lemonia Christina; Mporas, Iosif; Spyrelli, Evgenia; Lianou, Alexandra; Nychas, George-John.

In: IEEE Access, 08.06.2020.

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Fengou, Lemonia Christina ; Mporas, Iosif ; Spyrelli, Evgenia ; Lianou, Alexandra ; Nychas, George-John. / Estimation of the Microbiological Quality of Meat using Rapid and Non-Invasive Spectroscopic Sensors. In: IEEE Access. 2020.

Bibtex

@article{46ce8e40a0dc4e6d819b8b1560c301d9,
title = "Estimation of the Microbiological Quality of Meat using Rapid and Non-Invasive Spectroscopic Sensors",
abstract = "Spectroscopic 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.",
author = "Fengou, {Lemonia Christina} and Iosif Mporas and Evgenia Spyrelli and Alexandra Lianou and George-John Nychas",
note = "{\textcopyright} 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/.",
year = "2020",
month = jun,
day = "8",
doi = "10.1109/ACCESS.2020.3000690",
language = "English",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "IEEE",

}

RIS

TY - JOUR

T1 - Estimation of the Microbiological Quality of Meat using Rapid and Non-Invasive Spectroscopic Sensors

AU - Fengou, Lemonia Christina

AU - Mporas, Iosif

AU - Spyrelli, Evgenia

AU - Lianou, Alexandra

AU - Nychas, George-John

N1 - © 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/.

PY - 2020/6/8

Y1 - 2020/6/8

N2 - Spectroscopic 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.

AB - Spectroscopic 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.

U2 - 10.1109/ACCESS.2020.3000690

DO - 10.1109/ACCESS.2020.3000690

M3 - Article

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -