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

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.