Unsupervised classification of individual foodborne bacteria from a mixture of bacteria cultures within a hyperspectral microscope image

Salmonella is a leading cause of foodborne illness. Traditional detection methods require lengthy incubation periods or expensive reagent kits. Hyperspectral microscope images (HMIs) have been previously investigated as a method for early and rapid detection of bacteria by using a spectral signature...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Matthew Eady [verfasserIn] Bosoon Park [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	foodborne bacteria hyperspectral microscope images cluster analysis discriminant analysis distance classifier

Übergeordnetes Werk:	In: Journal of Spectral Imaging - IM Publications Open, 2017, 7(2018), 1, p a6
Übergeordnetes Werk:	volume:7 ; year:2018 ; number:1, p a6

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1255/jsi.2018.a6

Katalog-ID:	DOAJ010516743

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source	In Journal of Spectral Imaging 7(2018), 1, p a6 volume:7 year:2018 number:1, p a6
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topic_facet	foodborne bacteria hyperspectral microscope images cluster analysis discriminant analysis distance classifier Analytical chemistry
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authorswithroles_txt_mv	Matthew Eady @@aut@@ Bosoon Park @@aut@@
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callnumber-first	Q - Science
author	Matthew Eady
spellingShingle	Matthew Eady misc QD71-142 misc foodborne bacteria misc hyperspectral microscope images misc cluster analysis misc discriminant analysis misc distance classifier misc Analytical chemistry Unsupervised classification of individual foodborne bacteria from a mixture of bacteria cultures within a hyperspectral microscope image
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topic_title	QD71-142 Unsupervised classification of individual foodborne bacteria from a mixture of bacteria cultures within a hyperspectral microscope image foodborne bacteria hyperspectral microscope images cluster analysis discriminant analysis distance classifier
topic	misc QD71-142 misc foodborne bacteria misc hyperspectral microscope images misc cluster analysis misc discriminant analysis misc distance classifier misc Analytical chemistry
topic_unstemmed	misc QD71-142 misc foodborne bacteria misc hyperspectral microscope images misc cluster analysis misc discriminant analysis misc distance classifier misc Analytical chemistry
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title	Unsupervised classification of individual foodborne bacteria from a mixture of bacteria cultures within a hyperspectral microscope image
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title_full	Unsupervised classification of individual foodborne bacteria from a mixture of bacteria cultures within a hyperspectral microscope image
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title_sort	unsupervised classification of individual foodborne bacteria from a mixture of bacteria cultures within a hyperspectral microscope image
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title_auth	Unsupervised classification of individual foodborne bacteria from a mixture of bacteria cultures within a hyperspectral microscope image
abstract	Salmonella is a leading cause of foodborne illness. Traditional detection methods require lengthy incubation periods or expensive reagent kits. Hyperspectral microscope images (HMIs) have been previously investigated as a method for early and rapid detection of bacteria by using a spectral signature that is unique to the organism. Previous HMI use with bacteria has consisted of supervised classification with hypercubes collected for single culture images isolated from highly selective growth media. In order to move forward with HMI as a detection tool in the food industry, unsupervised classification of bacteria cells in mixed culture HMIs was investigated. Four foodborne bacteria cultures, S. Typhimurium (ST) E. coli (Ec), S. aureus (Sa) and L. innocua (Li) were combined in seven different culture combinations with HMIs collected between 450 nm and 800 nm. A k-means divisive cluster analysis (CA) was implemented and mixed culture image sets were found to contain between two and four clusters. CA cluster accuracy was obtained by assigning a dummy variable of the proposed CA classification, then carrying out a discriminant analysis. From the mixed culture HMIs, 700 bacteria cells were classified and accuracies were between 91.92% and 100%, with six of the seven HMI sets resulting in < 97% accuracies. A distance measure between clusters was applied to identify unknown clusters based on single culture reference samples of the four bacteria used. Results showed that the CA has potential for unsupervised classification of bacteria cells, but the distance metric was not an adequate method for identifying the unknown cluster based on reference spectra, potentially due to the collinearity amongst bacteria spectra.
abstractGer	Salmonella is a leading cause of foodborne illness. Traditional detection methods require lengthy incubation periods or expensive reagent kits. Hyperspectral microscope images (HMIs) have been previously investigated as a method for early and rapid detection of bacteria by using a spectral signature that is unique to the organism. Previous HMI use with bacteria has consisted of supervised classification with hypercubes collected for single culture images isolated from highly selective growth media. In order to move forward with HMI as a detection tool in the food industry, unsupervised classification of bacteria cells in mixed culture HMIs was investigated. Four foodborne bacteria cultures, S. Typhimurium (ST) E. coli (Ec), S. aureus (Sa) and L. innocua (Li) were combined in seven different culture combinations with HMIs collected between 450 nm and 800 nm. A k-means divisive cluster analysis (CA) was implemented and mixed culture image sets were found to contain between two and four clusters. CA cluster accuracy was obtained by assigning a dummy variable of the proposed CA classification, then carrying out a discriminant analysis. From the mixed culture HMIs, 700 bacteria cells were classified and accuracies were between 91.92% and 100%, with six of the seven HMI sets resulting in < 97% accuracies. A distance measure between clusters was applied to identify unknown clusters based on single culture reference samples of the four bacteria used. Results showed that the CA has potential for unsupervised classification of bacteria cells, but the distance metric was not an adequate method for identifying the unknown cluster based on reference spectra, potentially due to the collinearity amongst bacteria spectra.
abstract_unstemmed	Salmonella is a leading cause of foodborne illness. Traditional detection methods require lengthy incubation periods or expensive reagent kits. Hyperspectral microscope images (HMIs) have been previously investigated as a method for early and rapid detection of bacteria by using a spectral signature that is unique to the organism. Previous HMI use with bacteria has consisted of supervised classification with hypercubes collected for single culture images isolated from highly selective growth media. In order to move forward with HMI as a detection tool in the food industry, unsupervised classification of bacteria cells in mixed culture HMIs was investigated. Four foodborne bacteria cultures, S. Typhimurium (ST) E. coli (Ec), S. aureus (Sa) and L. innocua (Li) were combined in seven different culture combinations with HMIs collected between 450 nm and 800 nm. A k-means divisive cluster analysis (CA) was implemented and mixed culture image sets were found to contain between two and four clusters. CA cluster accuracy was obtained by assigning a dummy variable of the proposed CA classification, then carrying out a discriminant analysis. From the mixed culture HMIs, 700 bacteria cells were classified and accuracies were between 91.92% and 100%, with six of the seven HMI sets resulting in < 97% accuracies. A distance measure between clusters was applied to identify unknown clusters based on single culture reference samples of the four bacteria used. Results showed that the CA has potential for unsupervised classification of bacteria cells, but the distance metric was not an adequate method for identifying the unknown cluster based on reference spectra, potentially due to the collinearity amongst bacteria spectra.
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title_short	Unsupervised classification of individual foodborne bacteria from a mixture of bacteria cultures within a hyperspectral microscope image
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Unsupervised classification of individual foodborne bacteria from a mixture of bacteria cultures within a hyperspectral microscope image

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