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Systematic Approach to Reducing Errors in Deoxynivalenol Quantification: Insights from Bulk Wheat Sampling and Sample

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Accurate Deoxynivalenol (DON) quantification in bulk wheat is improved by a new protocol. This method optimizes sampling and analysis for reliable food safety testing.

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Area of Science:

  • Food Science
  • Analytical Chemistry
  • Agricultural Science

Background:

  • Accurate Deoxynivalenol (DON) quantification in wheat is vital for food safety.
  • Current methods lack reproducibility due to inconsistent sampling and analysis parameters.

Purpose of the Study:

  • To develop a representative measurement procedure for DON detection in truck-loaded bulk wheat.
  • To systematically analyze variability and errors in the full-process parameters for DON detection.

Main Methods:

  • Applied Monte Carlo simple random sampling principle to analyze variability across sampling stages.
  • Examined parameters for test portion, laboratory sample, composite sample, and primary sample.
  • Evaluated errors introduced at each step of the workflow.

Main Results:

  • Recommended a random distribution sampling method with >=11 points, each >=500g primary sample.
  • Specified composite sample homogenization (3x cone-and-quartering), >=750g laboratory sample, 1mm particle size, and 5g subsample for analysis.
  • Developed a protocol with a total relative error of 12.9%.

Conclusions:

  • The proposed protocol offers a practical, field-feasible approach for DON detection in truck-loaded wheat.
  • Minimizes error contribution from each parameter while maintaining low economic and time costs.
  • Enhances the reproducibility and accuracy of DON quantification in bulk wheat for food safety assurance.