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Susceptibility of Foodborne Pathogens to Milk-Origin Lactic Acid Bacteria Supernatants: A Comprehensive Meta-Regression Study.

Nathália FernandesYara LoforteVasco A P CadavezÚrsula Gonzales Barrón
Published in: Foods (Basel, Switzerland) (2024)
This systematic review and meta-analysis compile the in vitro antimicrobial efficacy of lactic acid bacteria (LAB) supernatants against three common pathogenic bacteria found in dairy products: Salmonella spp., L. monocytogenes , and Staphylococcus aureus . After screening and analysis of full papers, identified by searches in PubMed, Scopus, and Web of Science databases, thirty-nine studies were regarded as relevant, and a total of 510 observations were recorded. The effects of moderators on inhibition diameters were assessed by adjusting three pathogen-specific meta-regression models. Results showed that, in general terms, strains from the Enterococcus genus displayed the highest inhibition values against L. monocytogenes (15.90 ± 2.138 mm), whereas Lacticaseibacillus strains were more effective against S. aureus (11.89 ± 0.573 mm). The well diffusion test outperformed the spot and disk diffusion tests, and more acidic LAB supernatants resulted in higher measurements of inhibition diameters ( p < 0.001). Meta-regression models incorporating LAB genus, pathogen concentration, and incubation time explained 33.8%, 52.3%, and 19.8% of the total variance in inhibition diameters for L. monocytogenes, Salmonella spp., and S. aureus , respectively. None of the three models showed evidence of publication bias. This meta-regression study demonstrated that LAB strains present in dairy products possess a variable capacity to inhibit any of the three foodborne pathogens. Overall, L. monocytogenes was found to exhibit greater susceptibility than Salmonella spp. and S. aureus ; thus, the antilisterial capacity of the selected LAB strains could be exploited in developing biocontrol strategies for cheese-making.
Keyphrases
  • escherichia coli
  • lactic acid
  • staphylococcus aureus
  • biofilm formation
  • listeria monocytogenes
  • public health
  • candida albicans
  • machine learning