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Related Experiment Videos

Rice Safety Risk Data Generation Model Based on Gated Feature Transformation and Adaptive Diffusion.

Huawei Jiang1, Ruomeng Hu1, Wanbao Sheng1

  • 1College of Information Science and Engineering, Henan University of Technology, Zhengzhou, China.

Journal of Food Science
|June 4, 2026
PubMed
Summary
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This study introduces RADiff, a novel data generation model that creates high-fidelity rice contaminant data, effectively addressing data scarcity in food safety assessments. The model significantly improves risk evaluation by providing reliable data for analysis.

Area of Science:

  • Food Science and Technology
  • Data Science and Artificial Intelligence
  • Environmental Science

Background:

  • Food safety risk assessment for rice is crucial but hindered by data scarcity due to costly laboratory testing.
  • Effective evaluation of chemical contaminants like heavy metals and mycotoxins in rice requires comprehensive monitoring data.

Purpose of the Study:

  • To develop a high-fidelity tabular data generation model (RADiff) for rice risk assessment under small-sample conditions.
  • To address limitations in traditional data generation methods by incorporating adaptive mechanisms and advanced network architectures.

Main Methods:

  • Proposed RADiff model utilizing an adaptive layer normalization (AdaLN) mechanism for parameter adjustment.
  • Designed a residual gated cross transformation (RGCT) network to model interdependencies between heavy metals (Pb, Cd, iAs) and mycotoxins (AFB1, BaP).
Keywords:
adaptive diffusion modeldata augmentationrice safety risk assessmentsmall sample learningstatistical association between heavy metals and mycotoxins

Related Experiment Videos

  • Optimized the reverse diffusion process with skip sampling and an x0-prediction paradigm for enhanced efficiency and quality.

    • Main Results:

    • Generated data using RADiff demonstrated high consistency with real-world empirical data in statistical distribution.
    • RADiff significantly outperformed baseline models, including SMOTE, with lower Wasserstein distance (0.0029) and KL divergence (0.0061).
    • Incorporating generated data improved performance in downstream food safety risk classification tasks.

    Conclusions:

    • The RADiff model effectively alleviates the problem of limited rice sample data for food safety evaluations.
    • Generated data provides reliable support for downstream risk assessment tasks, enhancing the overall food safety framework.
    • The study validates the utility of advanced AI models in addressing real-world challenges in food safety monitoring and analysis.