# Tracing microbial hazards in the aquatic supply chain: challenges, technologies, and future directions

## TL;DR

This paper reviews how traceability technologies can improve microbial safety in aquatic products, addressing challenges and future directions for better food safety management.

## Contribution

The paper introduces the integration of genome sequencing and AI for real-time microbial source prediction in aquatic supply chains.

## Key findings

- Traceability systems with genome sequencing and AI improve detection speed and accuracy of microbial contamination.
- Challenges include technological barriers for small producers and fragmented international standards.
- Future efforts should focus on cost-effective tools and global standardization for improved food safety.

## Abstract

Aquatic products are a crucial source of dietary protein, especially in regions with abundant marine resources. However, with the expansion of global trade, the risk of microbial contamination in these products has increased, leading to serious public health concerns due to extended transportation and varying regulatory standards. Foodborne illnesses associated with aquatic products not only impact consumer health but also result in significant economic losses due to reduced market confidence, brand damage, and costly recalls. This review systematically examines the role of traceability technologies in enhancing microbial safety in aquatic products. Emphasis is placed on the integration of genome sequencing, artificial intelligence, and digital monitoring systems within the traceability framework. The evaluation considers specific performance indicators, including detection sensitivity (for example, the minimum limit of detection for target pathogens), source attribution resolution (for example, ≤20 core-genome SNP differences or unique wgMLST allelic profiles), and time-to-result in outbreak scenarios, as well as accessibility for small-scale producers and scalability across diverse aquaculture environments. In particular, we outline how artificial intelligence can be integrated with genome sequencing. For instance, WGS-derived genomic fingerprints can be transformed into machine learning models for rapid and highly sensitive microbial source prediction, thereby enhancing real-time decision-making capability along the aquatic product supply chain. Traceability systems have proven effective in enabling real-time monitoring and rapid response to contamination events. Technologies such as genome sequencing and AI significantly enhance detection speed and accuracy, contributing to improved food safety management. Nonetheless, challenges remain, including technological barriers for small-scale producers, fragmented international standards, and low public awareness. To overcome these limitations, future efforts should focus on developing cost-effective and user-friendly traceability tools, promoting global standardization, strengthening regulatory frameworks, and increasing public engagement. Furthermore, innovative approaches involving big data analytics, and AI hold great promise for advancing microbial safety and ensuring the integrity of aquatic product supply chains.