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Updated: Apr 15, 2026

Deep Neural Networks for Image-Based Dietary Assessment
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Multimodal large language models for food safety detection within deep learning frameworks: a review.

Haohan Ding1, Chengcheng Chen2, Xiaodong Song3

  • 1Science Center for Future Foods, Jiangnan University, Wuxi 214122, China; School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.

Food Chemistry
|April 13, 2026
PubMed
Summary

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Current research in food science·2026

Multimodal Large Models (MLLMs) offer advanced solutions for complex food safety challenges, improving intelligent supply chain monitoring. These models integrate diverse data for enhanced detection, addressing limitations of traditional methods.

Area of Science:

  • Food Science
  • Artificial Intelligence
  • Supply Chain Management

Background:

  • Food safety faces increasing complexity due to evolving industries and global trade.
  • Traditional detection methods (e.g., mass spectrometry) are accurate but slow, complex, and lack automation for modern supply chains.
  • There is a need for high-throughput, intelligent solutions for effective food safety monitoring.

Purpose of the Study:

  • To review the application of Multimodal Large Models (MLLMs) in food safety detection.
  • To summarize advancements in deep learning, cross-modal intelligence, and multimodal fusion for food safety.
  • To discuss current challenges and future directions for intelligent food safety systems.

Main Methods:

  • Review of recent advances in Multimodal Large Models (MLLMs).
Keywords:
Deep learningFood safetyMLLMsMultimodal fusion

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  • Integration of multi-modal perception, knowledge enhancement, and self-supervised pre-training.
  • Analysis of progress from deep learning to cross-modal intelligence and fusion mechanisms.

    • Main Results:

    • MLLMs show promise for intelligent food safety by integrating diverse data sources.
    • Applications include quality assessment and identification of upstream agricultural risks.
    • Progress in multimodal fusion mechanisms enhances detection capabilities.

    Conclusions:

    • MLLMs present a significant advancement over traditional methods for food safety.
    • Future research should focus on data scarcity, reliable intelligence, energy efficiency, and security.
    • Intelligent food safety detection systems are crucial for evolving global food systems.