Development of (TiO2-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 (TiO2) 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 0C. The migration characteristics were analyzed using inductively coupled plasma mass spectrometry (ICP-MS) at these 3 different temperature applications. The SEM results indicated that metal oxides had a substantially homogenous distribution in the polymer matrix, but agglomerates were formed in some regions. The Ti and Zn nanoparticles migrated to minced beef were significantly affected by the independent variables. The amounts of Ti and Zn migrated into minced beef samples ranged from 21.37 to 48.15 ppm and 11.01 and 52.74 ppm, respectively. In general, migration from Ti nanoparticles was greater compared to Zn nanoparticles. As a result, it has been demonstrated that TiO2 and ZnO nanoparticles are good alternatives to functionalizing food packaging film.

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. The isolated Fusarium strains were: F. solani, F. oxysporum, F. sambucinum, F. graminearum, F. coeruleum, F. sulphureum, F. auneaceum and F. culmorum. In general, it was observed that ethanolic extracts showed highest antifungal activity. The antifungal activity of extracts was evaluated with machine learning (ML) methods. Several ML methods (classification and regression trees (CART), support vector machines (SVM), k-Nearest Neighbors (k-NN), artificial neural network (ANN), ensemble algorithms (EA), AdaBoost (AB) algorithm, gradient boosting (GBM) algorithm, random forests (RF) bagging algorithm and extra trees (ET)) were applied and compared for modeling fungal growth. From this research, it is clear that ML methods have the lowest error level. As a result, ML methods are reliable, fast, and cheap tools for predicting the antifungal activity of gilaburu extracts. These encouraging results will attract more research efforts to implement ML into the field of food microbiology instead of traditional methods.

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. One of them was developed with artificial neural networks (ANN), and the other was formed with multiple linear regression (MLR). Four common training algorithms were used. Levenberg-Marquardt (LM), Bayesian regulation (BR), scaled conjugate gradient (SCG) and resilient back propagation (RP) were investigated, and the algorithms were compared. Root mean squared error and correlation coefficient were evaluated as performance criteria. When these criteria were analyzed, ANN showed high prediction performance, while MLR showed low prediction performance. As a result, it is seen that when the different input values are provided to the system developed with ANN, the most accurate inhibition zone (IZ) estimates were obtained. The results of this study could offer new perspectives, particularly in the field of microbiology, because these could be applied to other type of extraction, concentrations, and pathogens, without resorting to experiments.