MycotoxinDB

MycotoxinDB predicts masked mycotoxins produced by plant metabolism or food processing using reaction-rule-driven computational models to support identification and risk assessment.

Key Features:

• Reaction-rule prediction: Uses explicit chemical reaction rules to predict potential masked mycotoxins.
• Data-driven approach: Applies a data-driven methodology to prioritize and generate candidate masked compounds.
• Chemical transformation models: Implements computational models based on chemical transformation rules for identification and annotation.
• Rapid identification: Enables rapid identification of masked compounds with improved efficiency and accuracy compared to traditional methods.
• Empirical example: Has identified seven masked forms of deoxynivalenol (DON) from wheat samples.

Scientific Applications:

• Masked mycotoxin identification: Identification of masked mycotoxins in crop matrices, exemplified by DON in wheat.
• Risk assessment support: Facilitates identification and analysis required for mycotoxin risk assessment.
• Contamination research: Supports research to understand and mitigate mycotoxin contamination and associated impacts on animal welfare and productivity.

Methodology:

Applies reaction-rule-driven, data-driven computational models of chemical transformations to predict masked mycotoxins.