Investigation of rheological, physicochemical, and sensorial properties of traditional low-fat Doogh formulated.

Doogh is a fermented beverage made from yoghurt with water and salt. Similarly, drinks based on yoghurt are available in different countries with varying degrees of dilution, fat content, rheological properties, and taste. In this project, the use of mathematical calculations in describing rheological parameters from traditional low-fat Doogh enriched with Caspian Sea (Huso huso) gelatin (0.4 w/v %), xanthan hydrocolloids (0.4 w/v %), and their mixture at a ratio of 0.2:0.2 w/v % studied. Also, serum isolation, pH, and sensory evaluation of samples were investigated. Also, the relationship between apparent viscosity and temperature of Doogh samples using the Arrhenius equation was studied. The sensory evaluation revealed that the overall acceptance scores of the samples containing gelatin, xanthan, mix, and control were 4.31, 4.33, 4.58, and 4.12, respectively. The study on serum separation value showed control sample (45.07) and mix sample (0.84) at the end of 30 days. On the first day, the pH of the Doogh samples decreased with the addition of hydrocolloids, and this trend was time dependent. pH reduction was higher in Doogh with gelatin than in other samples. Mathematical calculations showed that the low-fat Doogh is a non-Newtonian type and shear-thinning (Pseudoplastic) fluid. The activation energy was calculated between 11.65 and 19.15 kJ/mol. According to the obtained results, it concluded that the use of two hydrocolloid compounds improved the physicochemical and sensory characteristics of the low-fat Doogh samples. Also, the Ostwald-de Waele mathematical model had a high correlation with the rheological behavior of the samples.

Modeling of drying kiwi slices and its sensory evaluation.

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.