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<p>The real-time monitoring of aquatic product spoilage is vital for global food safety. This review explores intelligent detection systems that integrate advanced sensors with novel equipment to track key spoilage markers like trimethylamine and pH shifts. These integrated systems utilize colorimetric, electrical, or spectroscopic signals to achieve high sensitivity, with some demonstrating detection limits below 10&#xa0;ppm for target volatiles. By incorporating artificial intelligence, the equipment can perform non-destructive freshness analysis in real-time, achieving accuracy rates over 90% and significantly reducing manual inspection costs. Future development aims to build a complete supply chain solution by networking these detection devices and transmitting data to blockchain platforms. Current limitations, however, include biofilm interference on sensor surfaces, signal drift in complex samples, and most systems are still confined to laboratory settings. The convergence of large language models for data analysis, bio-inspired recognition elements, self-calibration algorithms, and flexible electronics is poised to advance multimodal sensing equipment, pushing aquatic product quality control toward true real-time and precision management.</p>

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Real-time spoilage monitoring of aquatic products using advanced sensors and novel equipment

  • 期刊论文

  • 发表日期：

    2026年1月30日

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  • Qi Yu,
  • Min Zhang,
  • Arun S. Mujumdar,
  • Min Huang

摘要

The real-time monitoring of aquatic product spoilage is vital for global food safety. This review explores intelligent detection systems that integrate advanced sensors with novel equipment to track key spoilage markers like trimethylamine and pH shifts. These integrated systems utilize colorimetric, electrical, or spectroscopic signals to achieve high sensitivity, with some demonstrating detection limits below 10 ppm for target volatiles. By incorporating artificial intelligence, the equipment can perform non-destructive freshness analysis in real-time, achieving accuracy rates over 90% and significantly reducing manual inspection costs. Future development aims to build a complete supply chain solution by networking these detection devices and transmitting data to blockchain platforms. Current limitations, however, include biofilm interference on sensor surfaces, signal drift in complex samples, and most systems are still confined to laboratory settings. The convergence of large language models for data analysis, bio-inspired recognition elements, self-calibration algorithms, and flexible electronics is poised to advance multimodal sensing equipment, pushing aquatic product quality control toward true real-time and precision management.