Exploration of Foods and Foodomics

Table 1. Comparative characteristics and roles of analytical methods within the integrated framework.

Aspect	Conventional microbiology	Molecular methods (PCR/WGS)	Foodomics approaches
Detection target	Viable culturable pathogens	Specific genes, genomes, virulence markers	Microbial communities, metabolites, proteomes
Discriminatory resolution	Low-moderate (serotyping, biochemical)	High (SNP-level via WGS)	Very high (system-level, culture-independent)
Turnaround time	2–5 days (culture) to weeks	Hours (qPCR) to 1–3 days (WGS)	Variable: days (metabolomics) to weeks (metagenomics)
Key strengths	Gold standard for viability; essential for AMR testing; regulatory acceptance	High sensitivity/specificity; strain-level tracing; real-time surveillance integration	Culture-independent; holistic ecosystem view; characterizes pathogen-food matrix interactions
Key limitations	Cannot detect VBNC cells; limited strain discrimination; slow	Requires reference genome databases; cannot confirm viability	Bioinformatic complexity; high cost; limited regulatory standardization
Regulatory status	ISO/AOAC standardized; fully accepted	Increasingly accepted; WGS mandated in EU/US for select pathogens	Research-grade; regulatory integration emerging
Role in framework	Foundation: confirmation, AMR profiling, isolate preservation	Core: source attribution, phylogenomics, strain linkage	Extended: risk contextualization, contamination ecology, hypothesis generation

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AMR: antimicrobial resistance; PCR: polymerase chain reaction; qPCR: quantitative polymerase chain reaction; SNP: single-nucleotide polymorphism; VBNC: viable but non-culturable; 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

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

Not applicable.

Copyright

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References

World Health Organization. WHO estimates of the global burden of foodborne diseases: Foodborne Disease Burden Epidemiology Reference Group 2007-2015. Geneva: World Health Organization; 2015.

Havelaar AH, Kirk MD, Torgerson PR, Gibb HJ, Hald T, Lake RJ, et al.; World Health Organization Foodborne Disease Burden Epidemiology Reference Group. World Health Organization Global Estimates and Regional Comparisons of the Burden of Foodborne Disease in 2010. PLoS Med. 2015;12:e1001923. [DOI] [PubMed] [PMC]

Scallan E, Hoekstra RM, Angulo FJ, Tauxe RV, Widdowson MA, Roy SL, et al. Foodborne illness acquired in the United States--major pathogens. Emerg Infect Dis. 2011;17:7–15. [DOI] [PubMed] [PMC]

European Food Safety Authority (EFSA); European Centre for Disease Prevention and Control (ECDC). The European Union One Health 2022 Zoonoses Report. EFSA J. 2023;21:e8442. [DOI] [PubMed] [PMC]

Suzzi G, Corsetti A. Food Microbiology: The Past and the New Challenges for the Next 10 Years. Front Microbiol. 2020;11:237. [DOI] [PubMed] [PMC]

Todd EC. Epidemiology of foodborne diseases: a worldwide review. World Health Stat Q. 1997;50:30–50. [PubMed]

Swaminathan B, Gerner-Smidt P. The epidemiology of human listeriosis. Microbes Infect. 2007;9:1236–43. [DOI] [PubMed]

CDC. Foodborne Illness Source Attribution Estimates – United States, 2022 [Internet]. [cited 2026 Mar 24]. Available from: https://www.cdc.gov/ifsac/php/data-research/annual-report-2022.html

Allard MW, Strain E, Melka D, Bunning K, Musser SM, Brown EW, et al. Practical Value of Food Pathogen Traceability through Building a Whole-Genome Sequencing Network and Database. J Clin Microbiol. 2016;54:1975–83. [DOI] [PubMed] [PMC]

10.

den Bakker HC, Allard MW, Bopp D, Brown EW, Fontana J, Iqbal Z, et al. Rapid whole-genome sequencing for surveillance of Salmonella enterica serovar enteritidis. Emerg Infect Dis. 2014;20:1306–14. [DOI] [PubMed] [PMC]

11.

Ronholm J, Nasheri N, Petronella N, Pagotto F. Navigating Microbiological Food Safety in the Era of Whole-Genome Sequencing. Clin Microbiol Rev. 2016;29:837–57. [DOI] [PubMed] [PMC]

12.

Cifuentes A. Food analysis and foodomics. J Chromatogr A. 2009;1216:7109. [DOI] [PubMed]

13.

Hu C, Xu G. Mass-spectrometry-based metabolomics analysis for foodomics. Trends Analyt Chem. 2013;52:36–46. [DOI]

14.

Manning L, Morris W, Birchmore I. Organisational forgetting: The food safety risk associated with unintentional knowledge loss. Trends Food Sci Technol. 2021;118:242–51. [DOI]

15.

Jagadeesan B, Gerner-Smidt P, Allard MW, Leuillet S, Winkler A, Xiao Y, et al. The use of next generation sequencing for improving food safety: Translation into practice. Food Microbiol. 2019;79:96–115. [DOI] [PubMed] [PMC]

16.

Nasheri N, Vester A, Petronella N. Foodborne viral outbreaks associated with frozen produce. Epidemiol Infect. 2019;147:e291. [DOI] [PubMed] [PMC]

17.

Pires SM, Fischer-Walker CL, Lanata CF, Devleesschauwer B, Hall AJ, Kirk MD, et al. Aetiology-Specific Estimates of the Global and Regional Incidence and Mortality of Diarrhoeal Diseases Commonly Transmitted through Food. PLoS One. 2015;10:e0142927. [DOI] [PubMed] [PMC]

18.

Codex Alimentarius Commission. Principles and Guidelines for the Conduct of Microbiological Risk Assessment (CAC/GL 30-1999). Rome: FAO/WHO; 2014.

19.

Nauta MJ. Microbiological risk assessment models for partitioning and mixing during food handling. Int J Food Microbiol. 2005;100:311–22. [DOI] [PubMed]

20.

EFSA Panel on Biological Hazards (EFSA BIOHAZ Panel), Koutsoumanis K, Allende A, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Chemaly M, et al. Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food-borne microorganisms. EFSA J. 2019;17:e05898. [DOI] [PubMed] [PMC]

21.

Grace D. Food Safety in Low and Middle Income Countries. Int J Environ Res Public Health. 2015;12:10490–507. [DOI] [PubMed] [PMC]

22.

Hedberg CW. Foodborne illness acquired in the United States. Emerg Infect Dis. 2011;17:1338:1339–40. [DOI] [PubMed] [PMC]

23.

Painter JA, Hoekstra RM, Ayers T, Tauxe RV, Braden CR, Angulo FJ, et al. Attribution of foodborne illnesses, hospitalizations, and deaths to food commodities by using outbreak data, United States, 1998-2008. Emerg Infect Dis. 2013;19:407–15. [DOI] [PubMed] [PMC]

24.

ICMSF. Microorganisms in Foods 7: Microbiological Testing in Food Safety Management. 2nd ed. New York: Springer; 2018. [DOI]

25.

Carpentier B, Cerf O. Review--Persistence of Listeria monocytogenes in food industry equipment and premises. Int J Food Microbiol. 2011;145:1–8. [DOI] [PubMed]

26.

ISO 11290-1:2017. Microbiology of the food chain. Horizontal method for the detection and enumeration of Listeria monocytogenes and of Listeria spp.—Part 1: Detection method. Geneva: ISO; 2017.

27.

Law JW, Ab Mutalib NS, Chan KG, Lee LH. Rapid methods for the detection of foodborne bacterial pathogens: principles, applications, advantages and limitations. Front Microbiol. 2015;5:770. [DOI] [PubMed] [PMC]

28.

Malorny B, Tassios PT, Rådström P, Cook N, Wagner M, Hoorfar J. Standardization of diagnostic PCR for the detection of foodborne pathogens. Int J Food Microbiol. 2003;83:39–48. [DOI] [PubMed]

29.

Allard MW, Bell R, Ferreira CM, Gonzalez-Escalona N, Hoffmann M, Muruvanda T, et al. Genomics of foodborne pathogens for microbial food safety. Curr Opin Biotechnol. 2018;49:224–9. [DOI] [PubMed]

30.

Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. Shotgun metagenomics, from sampling to analysis. Nat Biotechnol. 2017;35:833–44. [DOI] [PubMed]

31.

Harvey DJ. Analysis of Protein Glycosylation by Mass Spectrometry. In: Analysis of Protein Post-Translational Modifications by Mass Spectrometry. Hoboken: Wiley; 2016. pp. 89–159. [DOI]

32.

Munck N, Njage PMK, Leekitcharoenphon P, Litrup E, Hald T. Application of Whole-Genome Sequences and Machine Learning in Source Attribution of Salmonella Typhimurium. Risk Anal. 2020;40:1693–705. [DOI] [PubMed] [PMC]

33.

Besser J, Carleton HA, Gerner-Smidt P, Lindsey RL, Trees E. Next-generation sequencing technologies and their application to the study and control of bacterial infections. Clin Microbiol Infect. 2018;24:335–41. [DOI] [PubMed] [PMC]

34.

Stavropoulou E, Bezirtzoglou E. Predictive Modeling of Microbial Behavior in Food. Foods. 2019;8:654. [DOI] [PubMed] [PMC]

35.

Todd EC, Greig JD, Bartleson CA, Michaels BS. Outbreaks where food workers have been implicated in the spread of foodborne disease. Part 3. Factors contributing to outbreaks and description of outbreak categories. J Food Prot. 2007;70:2199–217. [DOI] [PubMed]

36.

Kirby R, Teixeira P. Consumer food trends and food safety: Challenges in modern food systems. Trends Food Sci Technol. 2025;166:105398. [DOI]

37.

Haas CN, Rose JB, Gerba CP. Quantitative Microbial Risk Assessment. 2nd ed. Hoboken: John Wiley & Sons; 2014. [DOI]

38.

Buchanan R, Whiting R, Damert W. When is simple good enough: a comparison of the Gompertz, Baranyi, and three-phase linear models for fitting bacterial growth curves. Food Microbiol. 1997;14:313–26. [DOI]

39.

Chattaway MA, Greig DR, Gentle A, Hartman HB, Dallman TJ, Jenkins C. Whole-Genome Sequencing for National Surveillance of Shigella flexneri. Front Microbiol. 2017;8:1700. [DOI] [PubMed] [PMC]

40.

Koutsoumanis KP, Aspridou Z. Individual cell heterogeneity in Predictive Food Microbiology: Challenges in predicting a “noisy” world. Int J Food Microbiol. 2017;240:3–10. [DOI] [PubMed]

41.

Covello VT, Sandman PM. Risk communication: evolution and revolution. In: Wolbarst A, ed. Solutions to an Environment in Peril. Baltimore: Johns Hopkins University Press; 2001. pp. 164–78.

42.

FAO and WHO. Hazard Analysis and Critical Control Point (HACCP) System and Guidelines for its Application [Annex to General principles of food hygiene (CXC 1-1969)] [Internet]. Rome: FAO/WHO; c2023 [cited 2026 Mar 24]. Available from: https://doi.org/10.4060/cc6125en

43.

Fischhoff B, Brewer NT, Downs JS, editors. Communicating Risks and Benefits: An Evidence-Based User’s Guide. Silver Spring: FDA; 2011.

44.

Van Asselt MBA, Vos E. The Precautionary Principle and the Uncertainty Paradox. J Risk Res. 2006;9:313–36. [DOI]

45.

Thakur S, Gray GC. The Mandate for a Global “One Health” Approach to Antimicrobial Resistance Surveillance. Am J Trop Med Hyg. 2019;100:227–8. [DOI] [PubMed] [PMC]

46.

Franz E, Gras LM, Dallman T. Significance of whole genome sequencing for surveillance, source attribution and microbial risk assessment of foodborne pathogens. Curr Opin Food Sci. 2016;8:74–9. [DOI]

47.

Ribot EM, Fair MA, Gautom R, Cameron DN, Hunter SB, Swaminathan B, et al. Standardization of pulsed-field gel electrophoresis protocols for the subtyping of Escherichia coli O157:H7, Salmonella, and Shigella for PulseNet. Foodborne Pathog Dis. 2006;3:59–67. [DOI] [PubMed]

48.

Njage PMK, Henri C, Leekitcharoenphon P, Mistou MY, Hendriksen RS, Hald T. Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next-Generation Sequencing Data. Risk Anal. 2019;39:1397–413. [DOI] [PubMed] [PMC]

49.

Deng X, den Bakker HC, Hendriksen RS. Genomic Epidemiology: Whole-Genome-Sequencing-Powered Surveillance and Outbreak Investigation of Foodborne Bacterial Pathogens. Annu Rev Food Sci Technol. 2016;7:353–74. [DOI] [PubMed]

50.

Loman NJ, Pallen MJ. Twenty years of bacterial genome sequencing. Nat Rev Microbiol. 2015;13:787–94. [DOI] [PubMed]