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Modeling of mold inactivation via cold atmospheric plasma (CAP)

Summary

This research develops a mathematical model to predict how cold atmospheric plasma kills mold, which is important because molds produce toxins that harm human and animal health and damage food and buildings. The model uses equations to describe mold growth and plasma effects, allowing researchers to predict outcomes in minutes rather than waiting weeks for lab experiments. The study found that plasma is most effective when its killing power matches the mold’s natural growth rate, causing complete extinction.

Background

Molds are omnipresent pathogens that produce mycotoxins responsible for serious health problems and material degradation. Conventional inactivation methods like chemical treatment and heating have significant limitations including toxicity, heat-resistant mold strains, and nutritional loss. Cold atmospheric plasma (CAP) represents a promising non-thermal alternative operating at ambient temperatures with short processing times.

Objective

This study presents a mathematical model for the elimination of mold using cold atmospheric plasma, describing mold population evolution via a nonlinear logistic equation with density-dependent inactivation rate. The model aims to determine conditions under which mold colonies become extinct and to validate theoretical predictions against experimental data for Aspergillus brasiliensis.

Results

The analytical solution of the logistic equation with density-dependent inactivation rate successfully predicted mold coverage at arbitrary times. Results showed that when plasma inactivation rate equals the maximum natural mycelium growth rate, the mold colony becomes extinct after finite time. At t₀=72h plasma activation, complete inactivation occurred (I=r), while at t₀=117h, a 113-hour revitalization period was observed before renewed growth with modified growth rate.

Conclusion

The nonlinear logistic model with density-dependent inactivation rate effectively describes mold extinction processes and allows determination of critical parameters for successful inactivation. The model provides rapid computational predictions (minutes) compared to weeks of laboratory experiments, and is generalizable to other microorganisms and inactivation techniques.
  • Published in:Applied and Environmental Microbiology,
  • Study Type:Mathematical Modeling Study,
  • Source: 10.1128/aem.02102-24, PMID: 40183547
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