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Application of machine learning for predicting aflatoxin contamination risk in maize from Texas.

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Aflatoxin Proficiency Testing and Control in Kenya.

Timothy J Herrman1, Vivian Hoffmann2, Anne Muiruri3

  • 1Office of the Texas State Chemist, Texas A&M AgriLife Research, Texas A&M University System, College Station, Texas 77841, USA.

Journal of Food Protection
|December 20, 2019
PubMed
Summary

Texas A&M AgriLife Research implemented a quality systems approach to manage aflatoxin, enhancing food safety for 10 million Kenyans. This involved analyst training and laboratory accreditation, leading to improved accuracy in testing maize for aflatoxin contamination.

Keywords:
AflatoxinProficiency testingQualification

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Area of Science:

  • Food Science
  • Analytical Chemistry
  • Public Health

Background:

  • Aflatoxin contamination in maize poses a significant food safety risk, particularly in regions like Kenya.
  • Accurate measurement and management of aflatoxin are crucial for protecting public health and ensuring food security.
  • Existing laboratory testing methods require robust quality systems to ensure reliability and accuracy.

Purpose of the Study:

  • To introduce and evaluate a quality systems approach for accurate aflatoxin measurement and management.
  • To improve food safety for consumers by enhancing the competence of laboratory testing.
  • To assess the impact of implemented quality system elements on analytical accuracy in maize milling.

Main Methods:

  • Implemented quality system elements including analyst training, proficiency testing, and use of reference materials.
  • Commercial maize millers developed and implemented food safety plans.
  • AgriLife laboratory verification testing was conducted under ISO/IEC 17025:2005 accreditation.
  • Proficiency rounds with varying aflatoxin concentrations (5-40 μg/kg) were performed in 2014-2015.

Main Results:

  • Five laboratories achieved a z-score >3 in proficiency testing, indicating accurate measurement.
  • In 2015, 31 analysts from 15 maize mills qualified, with an average relative standard deviation of 19.2% for aflatoxin testing.
  • Independent verification showed improved measurement accuracy after the implementation of the quality systems approach.

Conclusions:

  • The quality systems approach effectively improved laboratory testing competence and accuracy for aflatoxin.
  • Enhanced analytical capabilities contribute to better food safety management in the maize value chain.
  • The initiative successfully improved food safety for approximately 10 million Kenyans through reliable aflatoxin monitoring.