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This study introduces a novel deep learning model for predicting grain and oil food safety risks. Integrating blockchain technology enhances data traceability and credibility, improving food quality management.

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Area of Science:

  • Food Science and Technology
  • Artificial Intelligence
  • Blockchain Technology

Background:

  • Ensuring grain and oil food quality and safety is vital for public health and societal development.
  • Traditional risk prediction models struggle with complex data and lack credibility due to centralized storage.
  • Effective early warning systems are needed for comprehensive food safety risk management.

Purpose of the Study:

  • To develop an advanced risk prediction model for grain and oil food quality and safety.
  • To enhance data authenticity, credibility, and traceability in food safety management.
  • To overcome the limitations of traditional models in handling nonlinear data and ensuring data integrity.

Main Methods:

  • A deep learning model combining Grey Relational Analysis (GRA) and Bayesian-optimized Tabular Neural Network (TabNet-BO) for precise risk prediction.
  • Integration of a blockchain-based traceability mechanism with smart contracts for secure data interaction and recording.
  • A storage optimization strategy uploading only exceeding data to the blockchain and encrypting non-exceeding data locally.

Main Results:

  • The proposed model achieves precise and rapid fine-grained risk prediction of food quality and safety data.
  • Blockchain integration ensures the authenticity and traceability of prediction results and exceeding data.
  • The system demonstrates improved transparency and credibility in food safety data management compared to existing models.

Conclusions:

  • The deep learning and blockchain integrated model significantly enhances prediction capabilities for grain and oil food safety.
  • This approach provides a robust solution for transparent and credible food safety data management.
  • The study offers a promising direction for advancing food quality control and public health protection.