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Methods for Controlling Microbial Growth

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Related Experiment Video

Updated: May 11, 2026

A High-throughput Platform for the Screening of Salmonella spp./Shigella spp.
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Advanced data analytics and "omics" techniques to control enteric foodborne pathogens.

Shraddha Karanth1, Abani K Pradhan2

  • 1Department of Nutrition and Food Science, University of Maryland, College Park, MD, United States.

Advances in Food and Nutrition Research
|March 1, 2025
PubMed
Summary
This summary is machine-generated.

Advanced data analytics, including machine learning, is crucial for analyzing omics data from enteric pathogens. This approach helps mitigate food safety risks and protect public health from foodborne illnesses.

Keywords:
Artificial intelligenceEnteric bacteriaFoodborne illnessMachine learningPathogenic microorganismWhole genome sequencing

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Area of Science:

  • Food safety
  • Microbiology
  • Data Science

Background:

  • Enteric bacterial pathogens cause millions of foodborne illnesses globally.
  • Subtypes of pathogens like E. coli, Listeria, and Salmonella exhibit significant variations.
  • Traditional methods struggle to keep pace with the complexity of pathogen behavior.

Purpose of the Study:

  • To review the application of advanced data analytics, specifically machine learning, in analyzing omics data of enteric bacterial pathogens.
  • To highlight the potential of these techniques in enhancing food safety and public health.
  • To identify future research directions in this domain.

Main Methods:

  • Utilizing "omics" technologies (genomics, proteomics, transcriptomics, metabolomics) to gather pathogen data.
  • Applying advanced data analytics, including machine learning and artificial intelligence, for data interpretation.
  • Reviewing current research on data analysis strategies for enteric pathogens.

Main Results:

  • Omics data provides deep insights into the genetic and phenotypic differences of enteric pathogens.
  • Machine learning and AI are increasingly effective in extracting actionable knowledge from complex pathogen datasets.
  • These advanced analytics aid in predicting and controlling pathogen prevalence in food systems.

Conclusions:

  • Advanced data analytics, particularly machine learning, is essential for interpreting omics data from enteric pathogens.
  • These methods offer powerful tools to mitigate food safety risks and protect public health.
  • Continued development and application of these techniques are vital for future food safety strategies.