GIS applications of microbiological contamination in seafood factories and food safety certifications surveillance with next generation sequencing technology

MeSTI (Food Safety is the Responsibility of the Industry) and HACCP (Hazard Analysis Critical Control Point are food safety certifications provided by Malaysia's Ministry of Health (MOH). Current food safety surveillance relies on traditional methods, including manual data collection and microbial culturing, which lead to inefficiencies and data loss. This research aimed to utilise Geographical Information Systems (GIS) and Next Generation Sequencing (NGS) to improve food safety certification surveillance. Quantum GIS (QGIS) was employed for spatial analysis of data from six seafood factories in Penang, Malaysia and secondary data from 4972 HACCP certifications and 768 Penang food factories. The MMQGIS plugin achieved a 98.31% geocoding success rate. Temporal visualisation from 2012 to 2021 showed early HACCP adoption and 93.91% saturation on Peninsular Malaysia by 2012, while East Malaysia began only in 2017. Buffering techniques in QGIS, applied within 1 to 5 km zones, revealed varying enforcement coverage across five Penang districts. The Central Province Wellesley, with 290 food factories, showed 1% to 19% coverage within this radius, while the Northeast district, with 85 factories, had more concentrated coverage ranging from 9.4% to 89%, highlighting spatial differences in enforcement reach. Microbial contamination was analysed using NGS and traditional methods. The "certified" group showed significant pre- and post-cleaning hygiene improvements 9p = 0.0276) compared to the "uncertified" group (p = 0.5073), with no foodborne pathogens found in the "certified" group. NGS analysis revealed higher alpha diversity indices the "certified" group with significant p-values for observed ASVs (0.0036), Faith's PD (0.0026) and Shannon indices (0.032). Beta diversity indices, including Bray Curtis, Unweighted UniFrac and Jaccard, also showed significant differences (p = 0.001). The integration of GIS and NGS provided robust tools for mapping contamination and understanding microbiome diversity. Automation via QGIS Model Designer enabled faster, reproducible spatial analysis workflows, with total processing times averaging 0.34 seconds. This combined approach offers a more comprehensive, efficient framework for food safety monitoring, with potential future applications incorporating machine learning for enhanced contamination prediction.

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