Modelling of survival curves in food microbiology using adaptive fuzzy inference neural networks

Vassilis S. Kodogiannis; Ilias Petrounias

doi:10.1109/CIMSA.2012.6269596

Modelling of survival curves in food microbiology using adaptive fuzzy inference neural networks

Vassilis S. Kodogiannis
, Ilias Petrounias

MSM Service Systems

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

The development of accurate models to describe and predict pressure inactivation kinetics of microorganisms is very beneficial to the food industry for optimization of process conditions. The need for "intelligent" methods to model highly nonlinear systems is long established. The architecture and learning scheme of a novel fuzzy logic system implemented in the framework of a neural network is proposed. The objective of this research is to investigate the capabilities of the proposed scheme, to predicting of survival curves of Listeria monocytogenes inactivated by high hydrostatic pressure in UHT whole milk. The network constructs its initial rules by clustering while the final fuzzy rule base is determined by competitive learning. The performance of the proposed scheme has been compared against neural networks and partial least squares models usually used in food microbiology. © 2012 IEEE.

Original language	English
Title of host publication	CIMSA 2012 - 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Proceedings\|CIMSA - IEEE Int. Conf. Comput. Intell. Meas. Syst. Appl., Proc.
Place of Publication	USA
Publisher	IEEE
Pages	35-40
Number of pages	5
ISBN (Print)	9781457717772
DOIs	https://doi.org/10.1109/CIMSA.2012.6269596
Publication status	Published - 2012
Event	2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2012 - Tianjin Duration: 1 Jul 2012 → …

Conference

Conference	2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2012
City	Tianjin
Period	1/07/12 → …

Keywords

clustering
neuro-fuzzy systems
partial least squares regression
predictive modelling

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10.1109/CIMSA.2012.6269596

Cite this

Kodogiannis, V. S., & Petrounias, I. (2012). Modelling of survival curves in food microbiology using adaptive fuzzy inference neural networks. In CIMSA 2012 - 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Proceedings|CIMSA - IEEE Int. Conf. Comput. Intell. Meas. Syst. Appl., Proc. (pp. 35-40). IEEE. https://doi.org/10.1109/CIMSA.2012.6269596

Kodogiannis, Vassilis S. ; Petrounias, Ilias. / Modelling of survival curves in food microbiology using adaptive fuzzy inference neural networks. CIMSA 2012 - 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Proceedings|CIMSA - IEEE Int. Conf. Comput. Intell. Meas. Syst. Appl., Proc.. USA : IEEE, 2012. pp. 35-40

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title = "Modelling of survival curves in food microbiology using adaptive fuzzy inference neural networks",

abstract = "The development of accurate models to describe and predict pressure inactivation kinetics of microorganisms is very beneficial to the food industry for optimization of process conditions. The need for {"}intelligent{"} methods to model highly nonlinear systems is long established. The architecture and learning scheme of a novel fuzzy logic system implemented in the framework of a neural network is proposed. The objective of this research is to investigate the capabilities of the proposed scheme, to predicting of survival curves of Listeria monocytogenes inactivated by high hydrostatic pressure in UHT whole milk. The network constructs its initial rules by clustering while the final fuzzy rule base is determined by competitive learning. The performance of the proposed scheme has been compared against neural networks and partial least squares models usually used in food microbiology. {\textcopyright} 2012 IEEE.",

keywords = "clustering, neuro-fuzzy systems, partial least squares regression, predictive modelling",

author = "Kodogiannis, \{Vassilis S.\} and Ilias Petrounias",

year = "2012",

doi = "10.1109/CIMSA.2012.6269596",

language = "English",

isbn = "9781457717772",

pages = "35--40",

booktitle = "CIMSA 2012 - 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Proceedings|CIMSA - IEEE Int. Conf. Comput. Intell. Meas. Syst. Appl., Proc.",

publisher = "IEEE",

address = "United States",

note = "2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2012 ; Conference date: 01-07-2012",

}

Kodogiannis, VS & Petrounias, I 2012, Modelling of survival curves in food microbiology using adaptive fuzzy inference neural networks. in CIMSA 2012 - 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Proceedings|CIMSA - IEEE Int. Conf. Comput. Intell. Meas. Syst. Appl., Proc.. IEEE, USA, pp. 35-40, 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2012, Tianjin, 1/07/12. https://doi.org/10.1109/CIMSA.2012.6269596

Modelling of survival curves in food microbiology using adaptive fuzzy inference neural networks. / Kodogiannis, Vassilis S.; Petrounias, Ilias.
CIMSA 2012 - 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Proceedings|CIMSA - IEEE Int. Conf. Comput. Intell. Meas. Syst. Appl., Proc.. USA: IEEE, 2012. p. 35-40.

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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AB - The development of accurate models to describe and predict pressure inactivation kinetics of microorganisms is very beneficial to the food industry for optimization of process conditions. The need for "intelligent" methods to model highly nonlinear systems is long established. The architecture and learning scheme of a novel fuzzy logic system implemented in the framework of a neural network is proposed. The objective of this research is to investigate the capabilities of the proposed scheme, to predicting of survival curves of Listeria monocytogenes inactivated by high hydrostatic pressure in UHT whole milk. The network constructs its initial rules by clustering while the final fuzzy rule base is determined by competitive learning. The performance of the proposed scheme has been compared against neural networks and partial least squares models usually used in food microbiology. © 2012 IEEE.

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KW - partial least squares regression

KW - predictive modelling

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DO - 10.1109/CIMSA.2012.6269596

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BT - CIMSA 2012 - 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Proceedings|CIMSA - IEEE Int. Conf. Comput. Intell. Meas. Syst. Appl., Proc.

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Kodogiannis VS, Petrounias I. Modelling of survival curves in food microbiology using adaptive fuzzy inference neural networks. In CIMSA 2012 - 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Proceedings|CIMSA - IEEE Int. Conf. Comput. Intell. Meas. Syst. Appl., Proc.. USA: IEEE. 2012. p. 35-40 doi: 10.1109/CIMSA.2012.6269596

Modelling of survival curves in food microbiology using adaptive fuzzy inference neural networks

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