ABSTRACT
The current study aims to co-encapsulate Shirazi thyme (Zataria multiflora) essential oil (ZEO) and nisin into chitosan nanogel as an antimicrobial and antioxidant agent to enhance the shelf-life of cheese. Chitosan-caffeic acid (CS-CA) nanogel was produced to co-encapsulate Zataria multiflora essential oil and nisin. This nanogel was characterized by dynamic light scattering (DLS), Fourier Transform Infrared (FTIR) spectroscopic analysis, X-ray diffraction (XRD) analysis, and scanning electron microscopy (SEM) images. The effect of free (TFZN) and encapsulated ZEO-nisin in chitosan nanogel (TCZN) on the chemical and microbiological properties of Iranian white cheese was assessed. The particle size, polydispersity index value (PDI), zeta potential, antioxidant activity, and encapsulation efficiency of the optimal chitosan-ZEO-nisin nanogel were 421.6 nm, 0.343, 34.0 mV, 71.06%-82.69%, and 41.3 ± 0.5%, 0.79 ± 0.06 mg/mL. respectively. FTIR and XRD approved ZEO and nisin entrapment within chitosan nanogel. The chitosan nanogel showed a highly porous surface with an irregular shape. The bioactive compounds of ZEO and nisin decreased the pH changes in cheese. On the 60th day of storage, the acidity of treated samples was significantly lower than that of control. Although the lowest anisidine index value was observed in samples treated with sodium nitrate (NaNO3) (TS), there was no significant difference between this sample and TCZN. The lowest microbial population was observed in TCZN and TS. After 60 days of ripening, Coliforms were not detected in the culture medium of TCZN and TS. The results can contribute to the development of a natural preservative with the potential for application in the dairy industry.
ABSTRACT
In this study, monolayer drying of kiwi slices was simulated by a laboratory-scale hot-air dryer. The drying process was carried out at three different temperatures of 50, 60, and 70°C. After the end of drying process, initially, the experimental drying data were fitted to the 11 well-known drying models. The results indicated that Two-term model gave better performance compared with other models to monitor the moisture ratio (with average R2 value equal .998). Also, this study used artificial neural network (ANN) in order to feasibly predict dried kiwi slices moisture ratio (y), based on the time and temperature drying inputs (x1, x2). In order to do this research, two main activation functions called logsig and tanh, widely used in engineering calculations, were applied. The results revealed that, logsig activation function base on 13 neurons in first and second hidden layers were selected as the best configuration to predict the moisture ratio. This network was able to predict moisture ratio with R2 value .997. Furthermore, kiwi slice favorite is evaluated by sensory evaluation. In this test, sense qualities as color, aroma, flavor, appearance, and chew ability (tissue brittleness) are considered.
