An escalated demand of minimally processed food and increased negative perception for synthetic preservative has led to a lookout for a natural preservative. Essential oils (EOs) are volatile and aromatic secondary metabolites of plants that have been tapped mainly for its flavour and fragrances and various biological properties such as antimicrobial and antioxidant. The constituents and antifungal potential of EOs have been reported widely in the present scientific literature. Moreover, the current scientific research dealing with the mode of action of EOs on fungal spores and mycelial cells are very scarce, unlike bacteria. The antimicrobial efficacy of EO in real food system may alter due to interaction with food matrix components. Besides, minimum alteration in sensory qualities while retaining its maximum activity is the most sought-after criteria for food preservation with EOs. If the oil is applied in excess to have better antimicrobial activity, it may end up having an unacceptable organoleptic impact on the food. Appropriate edible delivery systems of EOs as an emulsion is a probable approach to retain the maximum efficacy of EOs in the food system. Nano-emulsification of EO could increase its bioactivity due to increased bioavailability in the food matrix. The basis of this review is to provide an overview of current knowledge about the antifungal properties and antifungal mode of action of EOs, and to recognize the application of EO as nano-sized oil droplets in the food system.
The present study involved integration of artificial neural network (ANN) with genetic algorithm (GA) for predicting the optimized process parameters required for fluidized bed drying of green tea leaves. It had a layer each for input and output with linear activation function and two hidden layers with a sigmoid function. The feed forward back propagation method was used to train the developed model. The input parameters used by ANN for generalizing the drying process were temperature (50–80°C) and air flow velocity (7–9.5 m/s), and the output parameters were drying time, total color difference (TCD), and total phenolic content (TPC). The weights and bias values of trained ANN were used by GA to estimate the fitness function, which maximizes the TPC and minimizes the drying time and TCD. The optimum process condition of independent variables (80°C and 9 m/s) obtained from the hybrid ANN/GA was validated, and agreeable relationship between actual and predicted values with relative standard deviation (SD) of 5.7, 0.46, and 0.22 was found for drying time, TCD and TPC of dried leaves, respectively. Hence, under this optimal drying condition, best quality green tea can be obtained within the limits defined.Practical applicationsDrying is a popular unit operation being preferred to prolong shelf life of cash crops. Air drying is predominantly used at any given scale due to its economic feasibility. However, the quality parameters get compromised during the process of drying. Hence, an optimal drying condition is necessary to have dried product with intact nutritional and functional activity, alongside consumer acceptability. This was attempted using ANN and GA. Artificial neural network and genetic algorithm can estimate the optimum fluidized bed drying condition to have intact physical and chemical properties of green tea leaves. The results indicated that the developed ANN/GA drying model can efficiently estimate the values of quality parameters of dried green tea leaves, and also identify the optimal drying conditions for any new data.
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