Skip to main content
ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Characterization and Interventions for Foodborne Pathogens » Research » Publications at this Location » Publication #426648
Research Project: Detection, Quantification and Characterization Technologies for Foodborne Pathogens

Location: Characterization and Interventions for Foodborne Pathogens

Title: Assessment of cluster analysis of elastic light scatter profiles for the identification of foodborne bacteria

Author
item BARI, SANA - Lincoln University - New Zealand
item ZHANG, YUWEI - Lincoln University - New Zealand
item PATSEKIN, VALERY - Purdue University
item ROBINSON, J. PAUL - Purdue University
item RAJWA, BARTEK - Purdue University
item Gehring, Andrew
item Lindsay, James
item KULASIRI, DON - Lincoln University - New Zealand
item ON, STEPHEN L.W. - Lincoln University - New Zealand

Submitted to: Systematic and Applied Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/15/2025
Publication Date: 7/20/2025
Citation: Bari, S., Zhang, Y., Patsekin, V., Robinson, J., Rajwa, B., Gehring, A.G., Lindsay, J.A., Kulasiri, D., On, S. 2025. Assessment of cluster analysis of elastic light scatter profiles for the identification of foodborne bacteria. Systematic and Applied Microbiology. 48(5). https://doi.org/10.1016/j.syapm.2025.126641.
DOI: https://doi.org/10.1016/j.syapm.2025.126641

Interpretive Summary: Faster and more accurate identification of harmful bacteria in food is needed to avoid costs incurred from longer product hold times prior to release, medical treatment related to foodborne diseases, and/or penalties assessed by regulatory agencies. A relatively new food testing device called BEAM, which is based on elastic light scatter (ELS) profiling, has been demonstrated to rapidly (minutes) identify bacteria isolated from food. However, BEAM sometimes misreports identification for bacteria that form atypical growth colonies or are not included in ELS profile databases. Cluster analysis, a form of machine learning, was used to improve and optimize ELS profile databases; resulting identification of bacteria will increase the accuracy and speed of food safety testing by producers and regulators, alike, and may foster the development of improved data analysis.

Technical Abstract: Elastic Light Scatter (ELS) profiling is a novel approach for simultaneous detection and identification of bacteria cultured on solid agar media. The profiles comprise a range of different spectral features that can be used jointly or individually as a basis for comparison. We examined the utility of cluster analysis of ELS profiles for classification and identification of bacteria of relevance to foods. A total of 1581 colonies from 48 strains, representing 17 different species distributed among four genera, were examined. Each of three spectral features (Zernike moments, Pseudozernike moments, Patsekin elements) were used individually and in combination for the cluster analysis. Of these, a combination of Patsekin elements and Pseudozernike moments yielded clusters that best reflected the known taxonomic relationships among the strains examined. Evidence of genus-level markers of colony architecture was seen and there was a general consensus of clustering at the species level. Nonetheless, some phenotypic variation was seen that resulted in individual colonies clustering in an aberrant fashion. Cluster analysis of ELS profiles is a useful adjunctive tool for the classification and identification of bacteria and results may also be helpful in informing the development and improvement of other data analytical tools for ELS profile analysis.