Mapping spoilage microbiota in complex food systems: organisms, mechanisms, and omics-based characterization.

Authors

Atefeh Asadi, Inga Sarand, Pirjo Spuul, Séamus Fanning, Guerrino Macori

Published in

Food research international (Ottawa, Ont.). Volume 242. Issue Pt 1. Pages 119633. Oct 31, 2026. Epub Jun 08, 2026.

Abstract

Food spoilage is a major cause of food loss, while it remains less understood in complex, multi-component foods than in single-ingredient products. This review reframes spoilage in such foods as a community-driven ecological process, not simply the result of single dominant organisms, and argues that spoilage is best understood through microbial activity rather than microbial presence or relative abundance alone. We develop this framework around three central ideas: (i) ingredient-derived microbiotas interact within a shared matrix, so spoilage depends on microbial succession and competition during storage; (ii) predictions based on individual specific spoilage organisms often perform poorly in heterogeneous mixed foods; and (iii) taxonomic dominance does not necessarily indicate spoilage activity. On this basis, we examine key spoilage-associated groups, including Leuconostoc gelidum, Lactococcus piscium, Latilactobacillus sakei, Latilactobacillus curvatus, Pseudomonas spp., Enterobacteriaceae, yeasts, and moulds, and link them to characteristic metabolites and spoilage patterns under refrigerated and modified-atmosphere storage. We then evaluate analytical approaches, from culture-based methods and MALDI-TOF MS to 16S rRNA and ITS sequencing, shotgun metagenomics, and activity-resolved multi-omics, according to what each can and cannot reveal about viable populations, microbial activity, community succession, and spoilage causation. We also discuss how bioinformatic choices influence interpretation and why gene detection does not necessarily indicate spoilage activity. Finally, we propose an integrated framework for study design and data integration to support more reliable quality control and shelf-life assessment in complex food systems.

PMID:
42629006
Bibliographic data and abstract were imported from PubMed on 22 Aug 2026.

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