# Integrating Blockchain Traceability and Deep Learning for Risk Prediction in Grain and Oil Food Safety

## TL;DR

This paper introduces a model combining blockchain and deep learning to improve food safety risk prediction and traceability for grain and oil products.

## Contribution

A novel deep learning model integrated with blockchain traceability for enhanced food safety risk prediction and data credibility.

## Key findings

- The GRA and TabNet-BO model enables precise and rapid risk prediction for grain and oil food safety.
- Blockchain integration ensures data authenticity and traceability by recording only exceeding data.
- The model improves prediction capability and data transparency compared to traditional methods.

## Abstract

The quality and safety of grain and oil food are paramount to sustainable societal development and public health. Implementing early warning analysis and risk control is critical for the comprehensive identification and management of grain and oil food safety risks. However, traditional risk prediction models are limited by their inability to accurately analyze complex nonlinear data, while their reliance on centralized storage further undermines prediction credibility and traceability. This study proposes a deep learning risk prediction model integrated with a blockchain-based traceability mechanism. Firstly, a risk prediction model combining Grey Relational Analysis (GRA) and Bayesian-optimized Tabular Neural Network (TabNet-BO) is proposed, enabling precise and rapid fine-grained risk prediction of the data; Secondly, a risk prediction method combining blockchain and deep learning is proposed. This method first completes the prediction interaction with the deep learning model through a smart contract and then records the exceeding data and prediction results on the blockchain to ensure the authenticity and traceability of the data. At the same time, a storage optimization method is employed, where only the exceeding data is uploaded to the blockchain, while the non-exceeding data is encrypted and stored in the local database. Compared with existing models, the proposed model not only effectively enhances the prediction capability for grain and oil food quality and safety but also improves the transparency and credibility of data management.