A data-driven meat freshness monitoring and evaluation method using rapid centroid estimation and hidden Markov models

Liu, T; Zhang, W; Yuwono, M; Zhang, M; Ueland, M; Forbes, SL; Su, SW

A data-driven meat freshness monitoring and evaluation method using rapid centroid estimation and hidden Markov models

Liu, T

Zhang, W

Yuwono, M Zhang, M Ueland, M

Forbes, SL Su, SW

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Publisher:: ELSEVIER SCIENCE SA
Publication Type:: Journal Article
Citation:: Sensors and Actuators, B: Chemical, 2020, 311
Issue Date:: 2020-05-15

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Field	Value	Language
dc.contributor.author	Liu, T https://orcid.org/0000-0003-3827-7489
dc.contributor.author	Zhang, W https://orcid.org/0000-0002-2064-0326
dc.contributor.author	Yuwono, M
dc.contributor.author	Zhang, M
dc.contributor.author	Ueland, M https://orcid.org/0000-0002-9155-3502
dc.contributor.author	Forbes, SL
dc.contributor.author	Su, SW
dc.date.accessioned	2020-10-15T10:24:31Z
dc.date.available	2020-10-15T10:24:31Z
dc.date.issued	2020-05-15
dc.identifier.citation	Sensors and Actuators, B: Chemical, 2020, 311
dc.identifier.issn	0925-4005
dc.identifier.issn	0925-4005
dc.identifier.uri	http://hdl.handle.net/10453/143275
dc.description.abstract	© 2020 Elsevier B.V. The food industry needs a fast and stable method to monitor and evaluate the freshness of meat products. This paper proposes the use of a metal-oxide-semiconductor (MOS) sensor-based electronic nose with a data-driven approach, hidden Markov models (HMMs), to detect meat spoilage. The data-driven approach we proposed include: (i) A training algorithm using segmental rapid centroid estimation (RCE) to increase the overall stability of the HMM optimisation algorithm; (ii) A monitoring algorithm using a single HMM trained only by fresh samples to track the change of meat freshness in real-time when the storage condition is not specified; (iii) An HMM-based decoding algorithm to cluster the freshness level when the whole life-span data of meat stored in a specific storage condition is available. Then, HMM for each freshness level is trained and applied in parallel as freshness evaluation models to classify the tested meat samples. In freshness monitoring, case studies on fish, beef and chicken demonstrate the effectiveness and potential of this method. In freshness evaluation, we observed sensitivity and specificity of 96.32% ± 2.83% and 99.07% ± 0.69%, 99.09% ± 2.40% and 99.82% ± 0.48%, as well as, 99.35% ± 0.27% and 98.31% ± 0.71% respectively for fish, beef and chicken, which warrants further investigation.
dc.language	English
dc.publisher	ELSEVIER SCIENCE SA
dc.relation.ispartof	Sensors and Actuators, B: Chemical
dc.relation.isbasedon	10.1016/j.snb.2020.127868
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0205 Optical Physics, 0301 Analytical Chemistry, 0912 Materials Engineering
dc.subject.classification	Analytical Chemistry
dc.title	A data-driven meat freshness monitoring and evaluation method using rapid centroid estimation and hidden Markov models
dc.type	Journal Article
utslib.citation.volume	311
utslib.for	090805 Food Processing
utslib.for	0301 Analytical Chemistry
utslib.for	0205 Optical Physics
utslib.for	0301 Analytical Chemistry
utslib.for	0912 Materials Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - CFS - Centre for Forensic Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2020-10-15T10:24:20Z
pubs.publication-status	Published
pubs.volume	311

Abstract:

© 2020 Elsevier B.V. The food industry needs a fast and stable method to monitor and evaluate the freshness of meat products. This paper proposes the use of a metal-oxide-semiconductor (MOS) sensor-based electronic nose with a data-driven approach, hidden Markov models (HMMs), to detect meat spoilage. The data-driven approach we proposed include: (i) A training algorithm using segmental rapid centroid estimation (RCE) to increase the overall stability of the HMM optimisation algorithm; (ii) A monitoring algorithm using a single HMM trained only by fresh samples to track the change of meat freshness in real-time when the storage condition is not specified; (iii) An HMM-based decoding algorithm to cluster the freshness level when the whole life-span data of meat stored in a specific storage condition is available. Then, HMM for each freshness level is trained and applied in parallel as freshness evaluation models to classify the tested meat samples. In freshness monitoring, case studies on fish, beef and chicken demonstrate the effectiveness and potential of this method. In freshness evaluation, we observed sensitivity and specificity of 96.32% ± 2.83% and 99.07% ± 0.69%, 99.09% ± 2.40% and 99.82% ± 0.48%, as well as, 99.35% ± 0.27% and 98.31% ± 0.71% respectively for fish, beef and chicken, which warrants further investigation.

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http://hdl.handle.net/10453/143275