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Artificial Neural Network Prediction of Antiadhesion and Antibiofilm-Forming Effects of Antimicrobial Active Mushroom Extracts on Food-Borne Pathogens

Summary

This research explores how extracts from various mushroom species can help prevent harmful bacteria from forming biofilms (sticky bacterial colonies) on surfaces in food processing facilities. Using artificial intelligence, the researchers developed a model to predict how effective different mushroom extracts would be at preventing bacterial growth and attachment. This is important because bacterial biofilms are a major food safety concern and current chemical treatments can be harmful and lead to bacterial resistance. Impacts on everyday life: • Safer food products with fewer contamination risks • Natural alternatives to harsh chemical sanitizers in food processing • Potential reduction in foodborne illness outbreaks • More environmentally friendly food safety solutions • Cost-effective ways to keep food processing equipment clean

Background

The problem of microbial biofilms has become increasingly prominent alongside food, pharmaceutical, and healthcare industrialization. Conventional methods of biofilm control can lead to bacterial resistance, are costly, have low efficiency, and may negatively impact human health and the environment. There is an urgent need for new antibiofilm products, particularly ‘green’ agents of natural origin. Mushrooms have recently emerged as a potential source of antibiofilm compounds.

Objective

To apply Artificial Neural Network (ANN) modeling to analyze the anti-adhesion and anti-biofilm-forming effects of 40 extracts from 20 mushroom species against two important food-borne bacterial pathogens – Listeria monocytogenes and Salmonella enteritidis. The study aimed to develop a predictive model that could support industrial applications during initial stages of biofilm formation.

Results

The majority of tested mushroom extracts showed antimicrobial activity with minimal inhibitory concentrations mainly in the range of 10-20 mg/mL. The extracts demonstrated strong antibiofilm activity, with over 50% inhibition in most cases, particularly against L. monocytogenes. The ANN model showed high prediction quality with an overall coefficient of determination (r2) of 0.865 for inhibition of adhesion and biofilm formation. Alkali extracts generally showed higher antimicrobial and antibiofilm activity compared to water extracts.

Conclusion

The study successfully demonstrated that mushroom extracts are effective food-borne pathogenic bacteria adhesion and biofilm-forming control agents. The ANN models showed good predictive capability for antibiofilm behavior, providing a promising tool for industrial applications. The natural materials tested showed several benefits including water solubility, efficiency, and non-toxicity, while lacking bactericidal activity, making them suitable for food industry applications.
  • Published in:Antibiotics (Basel),
  • Study Type:Laboratory Research,
  • Source: 10.3390/antibiotics12030627
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