Exploration of Foods and Foodomics

Table 3. Comparison of the integrated framework with existing international guidelines.

Framework feature	WHO/FAO Codex guidelines	CDC/EFSA surveillance frameworks	This integrated framework (present review)
Investigative perspective	Clinical/epidemiological	Epidemiological + limited food tracing	Food-system & production-environment-centered; integrates farm-to-fork continuum explicitly
Pathogen detection approach	Culture-based reference methods	Culture + selective molecular (PCR, WGS)	Tripartite: culture → molecular (PCR/WGS) → foodomics (metagenomics/metabolomics); iterative layers
Source attribution methodology	Epidemiological case linkage; food questionnaires	WGS clustering; SNP-based phylogenomics	WGS + metagenomics + supply chain network analysis + GIS spatial tracing; multi-evidence convergence
Risk assessment integration	Retrospective QMRA post-outbreak	Post-detection risk profiling	Dynamic, iterative QMRA embedded throughout investigation; real-time dose–response updating
AI/ML applications	Not incorporated	Emerging; not systematically integrated	Explicitly incorporated: WGS-based source prediction, deep learning for metagenomics, network analysis for supply chains
GIS & spatial epidemiology	Optional; not structured	Recommended for cluster detection	Core structural component: spatial cluster analysis, supply chain geography mapping, contamination front tracking
Resource-limited settings (LMICs)	General principles only; no tiered guidance	High-income country focus	Explicit three-tier adaptive model (foundational/intermediate/advanced) with LMIC-specific implementation guidance
Informal food supply chains	Not addressed	Not addressed	Dedicated section: traditional markets, street food vendors, traceability challenges in unregulated sectors
Novelty basis	Regulatory compliance standard	Surveillance and WGS integration	First framework to explicitly integrate foodomics + AI/ML + GIS + LMIC adaptation + informal supply chains within a single iterative investigation architecture

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AI/ML: artificial intelligence and machine learning; GIS: geographic information systems; LMICs: low- and middle-income countries; PCR: polymerase chain reaction; QMRA: quantitative microbial risk assessment; SNP: single-nucleotide polymorphism; WGS: whole-genome sequencing.

Declarations

Acknowledgments

The authors wish to acknowledge the institutional support of the Indonesian Food and Drug Authority (BPOM) in facilitating this research. No external writing assistance was used. The authors are solely responsible for the content.

Author contributions

AS: Conceptualization, Methodology, Data curation, Writing—original draft, Writing—review & editing, Visualization. WY: Writing—original draft, Writing—review & editing. FUS: Writing—original draft, Writing—review & editing. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that there are no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

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Availability of data and materials

Not applicable.

Funding

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