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Sources of Food Contamination01:29

Sources of Food Contamination

Contamination of food by microbial agents and natural toxins poses significant risks to public health. These hazards can be introduced at various points across the food supply chain, ranging from environmental sources to processing and storage stages. Understanding these contamination pathways is critical for developing strategies to ensure food safety.Seafood is particularly vulnerable to contamination through both environmental exposure and microbial colonization. Toxins from harmful algal...

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FEDformer-Based Paddy Quality Assessment Model Affected by Toxin Change in Different Storage Environments.

Zihan Li1, Qingchuan Zhang1, Wei Dong1

  • 1National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China.

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Monitoring paddy storage environments is crucial for food safety. Five key factors and advanced AI models accurately predict quality changes, ensuring healthier grain.

Keywords:
FEDformerpaddypaddy quality change predictionpaddy storageprediction

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Area of Science:

  • Agricultural Science
  • Food Science
  • Artificial Intelligence

Background:

  • Paddy quality is essential for human health and is significantly impacted by storage conditions.
  • Fungal growth due to improper storage degrades grain quality and poses health risks.
  • Effective monitoring of storage environments is needed to maintain grain integrity.

Purpose of the Study:

  • To identify critical factors influencing paddy quality changes during storage.
  • To develop an accurate predictive model for paddy quality.
  • To establish a grading evaluation model for stored paddy.

Main Methods:

  • Analysis of grain storage monitoring data from over 20 regions.
  • Identification of five essential factors for predicting quality changes.
  • Integration of these factors with the FEDformer (Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting) model and k-medoids algorithm.
  • Construction of a paddy quality change prediction and grading evaluation model.

Main Results:

  • Five factors were identified as essential for predicting paddy quality changes.
  • The developed model demonstrated the highest accuracy and lowest error in predicting quality changes.
  • The study successfully created a robust paddy quality assessment tool.

Conclusions:

  • Monitoring and controlling the storage environment are critical for preserving paddy quality.
  • The developed AI-driven model offers a reliable solution for ensuring food safety.
  • Implementing these findings can help maintain high standards for stored grain.