Development of (TiO-ZnO)/LDPE based active nanocomposite films and detection of migration to minced beef during storage using response surface methodology.

In the current research, titanium dioxide (TiO) and zinc oxide (ZnO) nanoparticles using ethylene ethyl acrylate as compatibilizer are incorporated into low-density polyethylene (LDPE) to generate active nanocomposite films (ANFs) using melt blowing technique. The film properties such as colour, thickness, mechanical and thermal properties, surface morphology, and molecular composition were analyzed. Subsequently, minced beef samples were packed with ANFs, and migration was determined in 7-days storage period at 1 °C, 7 °C, and -18 C.

Machine learning approach for predicting the antifungal effect of gilaburu (Viburnum opulus) fruit extracts on Fusarium spp. isolated from diseased potato tubers.

This work addresses the mathematical model building to detect the diameter of the inhibition zone of gilaburu (Viburnum opulus L.) extract against eight different Fusarium strains isolated from diseased potato tubers. Gilaburu extracts were obtained with acetone, ethanol or methanol.

Prediction of the antimicrobial activity of walnut (Juglans regia L.) kernel aqueous extracts using artificial neural network and multiple linear regression.

The mathematical model was established to determine the diameter of inhibition zone of the walnut extract on the twelve bacterial species. Type of extraction, concentration, and pathogens were taken as input variables. Two models were used with the aim of designing this system.