In the present study, various blended films from polyvinyl alcohol (PVA) and pinto bean starch (PBS) were prepared and the selected film was used to fabricate an antimicrobial packaging film. Different essential oils (EOs) were also exposed to minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) tests to find the most efficient EO against a range of microorganisms. From the primary studies, the PVA:PBS (80:20) and cinnamon essential oil (CEO) were chosen. Afterward, the blend composite film reinforced by 1, 2, and 3% CEO and several, physical, mechanical, structural, and antimicrobial attributes were scrutinized. The results showed a significant modification of the barrier and mechanical properties of the selected blended films as a result of CEO addition. Scanning electron micrographs confirmed the incorporation and distribution of CEO within the film matrix. The X-ray diffraction (XRD) patterns and Fourier transform infrared (FTIR) spectra indicated the interaction of CEO and the PVA-PBS composite. The antibacterial of the tested bacteria showed a significant increase by increasing the CEO concentration within the control film. CEO-loaded films were more effective in controlling Gram-positive bacteria compared to Gram-negative bacteria. It can be concluded that PVA-PBS-CEO films are promising candidates to produce biodegradable functional films for food and biomedical applications.
In this work, an artificial neural network (ANN) has
been utilized
to predict the surface tension of binary mixtures at different temperatures
and concentrations and at atmospheric pressure. It has been shown
that a multilayer perceptron network (MLP) can be trained better than
other types of ANNs, and it can therefore be used as a predictive
tool to predict the thermo-physical properties. In the modeling procedure,
60% of the available experimental data has been selected as the training
set; the remaining data has been used to test and validate the network.
After training and testing, the artificial neural network has been
used for the prediction of the surface tension of a number of other
systems, for which a minimum imprecision of 1.8% has been obtained.
The results obtained from the trained network have also been compared
to those obtained from the Sprow and Prausnitz model [Sprow, F.B.,
Prausnitz, J.M., Surface tensions of simple liquid mixtures. Trans. Faraday Soc. 1966, 62a 1097–1104]. It has been shown that the trained MLP network
can predict the experimental data better than the r conventional neural
network method while only a minimum number of adjustable parameters
have been used, compared to the number of adjustable parameters in
the thermodynamics models, such as the Sprow and Prausnitz model.
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