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Microbial-Maximum Likelihood Estimation Tool for Microbial Quantification in Food From Left-Censored Data Using

Gyung Jin Bahk1, Hyo Jung Lee2

  • 1Department of Food and Nutrition, Kunsan National University, Gunsan, South Korea.

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Summary

A new Microbial-MLE Tool addresses challenges in food microbiology by accurately analyzing bacterial counts below detection limits. This tool improves quantitative microbial risk assessment by overcoming limitations of traditional methods for censored data.

Keywords:
Excel spreadsheetlimit of quantification (LOQ)microbial censored datamicrobial measurementmicrobial risk assessment (MRA)non-detection (ND)

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Area of Science:

  • Food Microbiology
  • Statistical Analysis
  • Quantitative Microbial Risk Assessment

Background:

  • Bacterial counts below the limit of quantification (LOQ) or limit of detection (LOD) in food samples are often ignored or substituted, leading to inaccurate quantitative results.
  • Traditional methods for handling censored microbiological data can result in over or underestimation.
  • Maximum Likelihood Estimation (MLE) and Bayesian models are suitable for censored data, but practical tools for MLE in food microbiology are scarce.

Purpose of the Study:

  • To develop a user-friendly tool for implementing Maximum Likelihood Estimation (MLE) in food microbiology.
  • To provide a practical solution for analyzing censored microbiological data, specifically bacterial counts below LOQ/LOD.
  • To improve the accuracy of quantitative microbial risk assessment (MRA) by addressing censored data challenges.

Main Methods:

  • Development of a user-friendly "Microbial-MLE Tool" using an Excel spreadsheet.
  • The tool adjusts log-normal distributions to observed bacterial counts, accommodating censored data.
  • Implementation of the tool demonstrated through two case studies using real food microbial laboratory measurements.

Main Results:

  • The Microbial-MLE Tool offers an accessible and comprehensible method for performing MLE in food microbiology.
  • The tool effectively handles censored data, providing more accurate estimations compared to traditional approaches.
  • Case studies validated the tool's utility in real-world food microbial analysis.

Conclusions:

  • The Microbial-MLE Tool enhances the statistical analysis of censored microbiological data in food.
  • This accessible tool can significantly improve the reliability of quantitative microbial risk assessments.
  • The development addresses a critical need for practical statistical solutions in applied food microbiology.