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A probabilistic approach to the analysis of elastic light scatter profiles for identification of culturable bacteria.

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Τίτλος: A probabilistic approach to the analysis of elastic light scatter profiles for identification of culturable bacteria.
Συγγραφείς: Bari S; Dept. Wine, Food and Molecular Biosciences, Lincoln University, Lincoln 7647, New Zealand., Zhang Y; Dept. Wine, Food and Molecular Biosciences, Lincoln University, Lincoln 7647, New Zealand., Patsekin V; Dept. Basic Medical Sciences, Purdue University, West Lafayette, IN, USA., Robinson JP; Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA., Rajwa B; Dept. Basic Medical Sciences, Purdue University, West Lafayette, IN, USA., Kulasiri D; Dept. Wine, Food and Molecular Biosciences, Lincoln University, Lincoln 7647, New Zealand., On SLW; Dept. Wine, Food and Molecular Biosciences, Lincoln University, Lincoln 7647, New Zealand. Electronic address: stephen.on@lincoln.ac.nz.
Πηγή: Systematic and applied microbiology [Syst Appl Microbiol] 2026 Jul; Vol. 49 (4), pp. 126740. Date of Electronic Publication: 2026 Jun 28.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Elsevier GmbH Country of Publication: Germany NLM ID: 8306133 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1618-0984 (Electronic) Linking ISSN: 07232020 NLM ISO Abbreviation: Syst Appl Microbiol Subsets: MEDLINE
Imprint Name(s): Publication: [Munich, Germany] : Elsevier GmbH
Original Publication: Stuttgart ; New York : G. Fischer Verlag, c1983-
Ιατρικοί όροι (MeSH): Bacteria*/classification , Bacteria*/isolation & purification , Bacteria*/growth & development , Bacterial Typing Techniques*/methods , Scattering, Radiation* , Food Microbiology*, Light ; Phylogeny
Περίληψη: Elastic Light Scatter (ELS) profiling is a unique, non-invasive technique for the rapid analysis and identification of bacterial colonies grown on agar. Colonies are inspected using a laser; the resulting light-scatter images have been shown to be species-specific. We examined a probabilistic approach to the analysis of ELS profiles for identification purposes. We analyzed 1701 colonies representing 52 strains across 19 food-related bacterial species representing four phylogenetically distinct families. Each of three ELS-derived feature sets (Patsekin elements, Zernike moments, pseudo-Zernike moments) were included, yielding a total of 2257 features. Each individual feature was binarized using an adaptive threshold set at 50% of its maximum value, a strategy that preserved meaningful differences across feature types of varied scale. A species-level identification matrix was constructed by summarizing positively expressed features across colonies, and representative species profiles were generated for comparison. Several taxa sharing ecological niches and ELS features were combined for improved performance. Use of Hypothetical Median Organism - and Leave-one-out analyses subsequently correctly identified each of 15 defined taxa with high Willcox probability scores. Importantly, test species not included in the database (Arcobacter bilvalviorum, A. faecis, and A. ellisii) were correctly considered as unknown based on a combined analysis of probabilistic scores and taxonomic distances, even when profiles shared partial similarity. A probabilistic approach to identifying bacteria from ELS profiles is an effective means to achieve this goal, and could potentially be used for other methods that generate taxon-specific spectra.
(Copyright © 2026 The Authors. Published by Elsevier GmbH.. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: Elastic light scatter; Foodborne pathogens; Probabilistic identification; bacteria
Entry Date(s): Date Created: 20260629 Date Completed: 20260704 Latest Revision: 20260704
Update Code: 20260705
DOI: 10.1016/j.syapm.2026.126740
PMID: 42372419
Βάση Δεδομένων: MEDLINE
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  Data: A probabilistic approach to the analysis of elastic light scatter profiles for identification of culturable bacteria.
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  Data: <searchLink fieldCode="AU" term="%22Bari+S%22">Bari S</searchLink>; Dept. Wine, Food and Molecular Biosciences, Lincoln University, Lincoln 7647, New Zealand.<br /><searchLink fieldCode="AU" term="%22Zhang+Y%22">Zhang Y</searchLink>; Dept. Wine, Food and Molecular Biosciences, Lincoln University, Lincoln 7647, New Zealand.<br /><searchLink fieldCode="AU" term="%22Patsekin+V%22">Patsekin V</searchLink>; Dept. Basic Medical Sciences, Purdue University, West Lafayette, IN, USA.<br /><searchLink fieldCode="AU" term="%22Robinson+JP%22">Robinson JP</searchLink>; Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.<br /><searchLink fieldCode="AU" term="%22Rajwa+B%22">Rajwa B</searchLink>; Dept. Basic Medical Sciences, Purdue University, West Lafayette, IN, USA.<br /><searchLink fieldCode="AU" term="%22Kulasiri+D%22">Kulasiri D</searchLink>; Dept. Wine, Food and Molecular Biosciences, Lincoln University, Lincoln 7647, New Zealand.<br /><searchLink fieldCode="AU" term="%22On+SLW%22">On SLW</searchLink>; Dept. Wine, Food and Molecular Biosciences, Lincoln University, Lincoln 7647, New Zealand. Electronic address: stephen.on@lincoln.ac.nz.
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  Data: <searchLink fieldCode="MM" term="%22Bacteria%22">Bacteria*</searchLink>/<searchLink fieldCode="MM" term="%22Bacteria+classification%22">classification</searchLink> <br /><searchLink fieldCode="MM" term="%22Bacteria%22">Bacteria*</searchLink>/<searchLink fieldCode="MM" term="%22Bacteria+isolation+%26+purification%22">isolation & purification</searchLink> <br /><searchLink fieldCode="MM" term="%22Bacteria%22">Bacteria*</searchLink>/<searchLink fieldCode="MM" term="%22Bacteria+growth+%26+development%22">growth & development</searchLink> <br /><searchLink fieldCode="MM" term="%22Bacterial+Typing+Techniques%22">Bacterial Typing Techniques*</searchLink>/<searchLink fieldCode="MM" term="%22Bacterial+Typing+Techniques+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Scattering%2C+Radiation%22">Scattering, Radiation*</searchLink> <br /><searchLink fieldCode="MM" term="%22Food+Microbiology%22">Food Microbiology*</searchLink><br /><searchLink fieldCode="MH" term="%22Light%22">Light</searchLink> ; <searchLink fieldCode="MH" term="%22Phylogeny%22">Phylogeny</searchLink>
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  Data: Elastic Light Scatter (ELS) profiling is a unique, non-invasive technique for the rapid analysis and identification of bacterial colonies grown on agar. Colonies are inspected using a laser; the resulting light-scatter images have been shown to be species-specific. We examined a probabilistic approach to the analysis of ELS profiles for identification purposes. We analyzed 1701 colonies representing 52 strains across 19 food-related bacterial species representing four phylogenetically distinct families. Each of three ELS-derived feature sets (Patsekin elements, Zernike moments, pseudo-Zernike moments) were included, yielding a total of 2257 features. Each individual feature was binarized using an adaptive threshold set at 50% of its maximum value, a strategy that preserved meaningful differences across feature types of varied scale. A species-level identification matrix was constructed by summarizing positively expressed features across colonies, and representative species profiles were generated for comparison. Several taxa sharing ecological niches and ELS features were combined for improved performance. Use of Hypothetical Median Organism - and Leave-one-out analyses subsequently correctly identified each of 15 defined taxa with high Willcox probability scores. Importantly, test species not included in the database (Arcobacter bilvalviorum, A. faecis, and A. ellisii) were correctly considered as unknown based on a combined analysis of probabilistic scores and taxonomic distances, even when profiles shared partial similarity. A probabilistic approach to identifying bacteria from ELS profiles is an effective means to achieve this goal, and could potentially be used for other methods that generate taxon-specific spectra.<br /> (Copyright © 2026 The Authors. Published by Elsevier GmbH.. All rights reserved.)
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  Data: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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  Data: <i>Keywords: </i>Elastic light scatter; Foodborne pathogens; Probabilistic identification; bacteria
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