Inhibitory effect of thyme and cinnamon essential oils on Aspergillus flavus: Optimization and activity prediction model development

- Antifungal effect of thyme and cinnamon essential oils (EOs) was investigated.
- Individiual EOs and their mixtures inhibited growth of Aspergillus flavus.
- Response surface methodology (RSM) was applied for antifungal effect prediction.
- Artificial neural network with genetic algorithm (ANN-GA) was applied for antifungal effect prediction.
- The ANN-GA model was superior to RSM in terms of accuracy.

Antifungal effect of individual thyme (Thymus vulgaris L.) and cinnamon (Cinnamomum cassia L.) essential oils (EOs) and mixture of thereof on Aspergillus flavus spores was investigated. In order to optimize the process variables (time of action, concentration of individual or mixture EOs and their mass ratio) for the antifungal effect of EO mixture, two models were developed: the response surface methodology (RSM) and artificial neural network (ANN) combined with genetic algorithm (GA). In RSM model, three factors were involved in Box-Behnken design that was applied for the experiment. Based on the mean relative percent deviation (MRPD), both models provided a good quality prediction for the antifungal effect in terms of all three process variables. RSM and ANN-GA techniques predicted the 0.5% as an optimum percentage concentration of EO mixture in EOs mass ratio T. vulgaris:C. cassia 1:1, ensuring the highest antifungal effect of 95.8% and 96.4% after 65 min. Both models were found useful for the optimization of the antifungal effect in vitro. ANN-GA was found more accurate in comparison to RSM due to its lower value of MRPD. Therefore, ANN-GA can be generally used for optimization and prediction of antimicrobial effects of EOs and their mixtures.