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Formulation of Trichoderma spp. encapsulated in alginate: potential for biofungicide with controlled conidial release.

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Microbial bioproduct stability was enhanced using microencapsulation and artificial neural networks. This approach aids in predicting fungal viability during storage, optimizing bioproduct development.

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Area of Science:

  • Microbiology
  • Computational Biology
  • Biotechnology

Background:

  • Microbial bioproduct development necessitates understanding cell stability and viability during storage.
  • Trichoderma spp. are valuable microorganisms with applications in various bioproducts.
  • Piauí Cerrado native plants harbor diverse microbial communities.

Purpose of the Study:

  • To evaluate the viability of encapsulated Trichoderma spp. strains from the Piauí Cerrado.
  • To integrate microencapsulation techniques with computational modeling for stability assessment.
  • To explore the potential of artificial neural networks in predicting microbial viability over time.

Main Methods:

  • Microencapsulation of Trichoderma spp. using ionic gelation in a sodium alginate matrix.
  • Monitoring fungal viability via plating and conidial counting over 60 days.
  • Training a Multilayer Perceptron (MLP) Artificial Neural Network with specific regularization and dropout techniques.

Main Results:

  • Strains UFPI07, UFPI10, UFPI11, and UFPI16 demonstrated superior viability (>7 log CFU mL⁻¹).
  • Strains UFPI06 and UFPI18 showed a more significant decline in viability.
  • The MLP model provided exploratory projections of microbial trends beyond the experimental period.

Conclusions:

  • The combination of microencapsulation and machine learning offers a promising tool for estimating temporal microbial behavior.
  • This integrated approach can aid in optimizing bioproduct formulations and reducing experimental costs.
  • It represents an exploratory methodological advancement for predictive microbiology and bioproduct development.