THIN-LAYER DRYING KINETICS OF SESAME HULLS UNDER FORCED CONVECTION AND OPEN SUN DRYING

Sesame hulls are a useful by‐product of the sesame processing industry. The sesame hulls are produced at a high moisture content (68% wet basis) and need further drying to prevent deterioration. In this study, both open sun drying (OSD) and forced convection drying (FCD) at 42, 55, and 76C and 1.2 m/s air velocity were investigated. Six common thin‐layer drying models were fitted to the experimental data. Several statistical parameters were used to evaluate the performance of thin‐layer drying models, including r², x², root mean square error (RMSE) and residuals. Sesame hull drying was found to take place completely in the falling rate region. The modified Page model was found to describe OSD data well, while the Wang and Singh model was the best model for describing FCD. Effective diffusivity was found to be 1.89 × 10^‐8 m²/s and 7.36 × 10^‐10 to 1.20 × 10^‐9 m²/s for OSD and FCD, respectively. Activation energy was also found to be 12.95 kJ/mol for FCD.     

The effects of inoculant variables on the physicochemical and organoleptic properties of Doogh

The combined effects of probiotic sequential inoculation (before or after fermentation), type of native Bifidobacterium strain (Bifidobacterium animalis spp. lactis PTCC 1631 and Bifidobacterium bifidum PTCC 1644) and final pH of fermentation (4.5 or 4.2) on qualitative aspects of typical Iranian fermented milk drink (Doogh) were investigated. The blends inoculated with yoghurt bacteria only and with final fermentation pH of 4.5 had the shortest fermentation time. The highest viability of bifidobacteria in all treatments at the end of fermentation and throughout the refrigerated storage was observed for B_lY‐40‐4.5 (B. lactis co‐cultured with yoghurt starter bacteria and incubated at 40 °C until final pH of 4.5) and then for B_bY‐40‐4.5 (containing B. bifidum). The most acceptable organoleptic properties were observed for treatments B_lY‐40‐4.2 and B_bY‐40‐4.2, and then for B_bY‐40‐4.5.     

ANTIOXIDANT ACTIVITY OF BENE HULL OIL COMPARED WITH SESAME AND RICE BRAN OILS DURING THE FRYING PROCESS OF SUNFLOWER OIL

The antioxidant activity of Bene hull oil (BHO) was compared with that of sesame oil (SEO) and rice bran oil (RBO) during the frying process of sunflower oil (SFO) at 180C. The ratio between the polyunsaturated and saturated fatty acids and calculated oxidizability were statistically greater for the SFO (4.26 and 6.48, respectively), followed by the SEO (3.18 and 6.27, respectively), RBO (1.53 and 4.37, respectively) and BHO (0.37 and 1.67, respectively). Peroxide and acid values of the oils studied ranged from 0.34 to 3.07 mEq/kg and from 0.19 to 5.20 mg/g, respectively. Total tocopherols and phenolics contents of the SEO (1093.28 and 1042.43 mg/kg, respectively) were significantly higher than those of the SFO (740.27 and 38.68 mg/kg, respectively), BHO (573.41 and 276.67 mg/kg, respectively), and RBO (832.98 and 67.98 mg/kg, respectively). In total, based on the conjugated diene value and carbonyl value measurements during the frying process, the BHO showed an antioxidant activity higher than those of the SEO and RBO at the level of 2%, and the levels higher than 2% of the antioxidative oils caused to decrease the oxidative stability of the SFO, indicating the pro-oxidant effect of the oils added at these levels.

PRACTICAL APPLICATIONS

This research introduces the oil obtained from the hull of Bene, a species of pistachio growing wild in Iran, as a new source of vegetable oil for human nutrition with antioxidant activity significantly higher than sesame and rice bran oils, which are considered as very stable and antioxidative vegetable oils because of the presence of very strong indigenous antioxidants, during the frying process.     

The Binary System CaO–Nb2O5

The binary system CaO-Nb₂O₅ has been shown to include three compounds: CaO·Nb₂O₅, which melts congruently at 1560°C; 2CaO·Nb₂O₅, which melts congruently at 1576°C; and 3CaO·-Nb₂O₅, which melts incongruently at 1560°C. Three eutectic compositions occur at 6% CaO (1362°C), 23% CaO (1492°C), and 34% CaO (1535°C). These results were obtained by the cone-fusion technique. The compound 3CaO-Nb₂O₅ has been shown to exist in two forms: type I, face-centered cubic with a = 7.978 A, having a superlattice with a = 23.934 A, and type II, orthorhombic (pseudotetragonal, distorted cubic) with a = 11.51 A, b = 11.10 A, c = 15.98 A, having a pseudocell with a = 5.754 A, b = 5.551 A, and c = 7.990 A. The conditions controlling the formation of these two forms were not determinable from the experiments conducted.     

Effect of strain rate on the fracture behaviour of epoxy–graphene nanocomposite

In this study, epoxy-based nanocomposite was fabricated by the addition of graphene nanosheet via a solution casting method. To investigate the effect of strain rate on tensile properties of epoxy, tensile tests were done on standard samples at different strain rates (0.05–1 min^?1). The role of strain rate and presence of graphene on fracture behaviour of epoxy were also studied by investigation of the fracture surfaces of some samples by scanning electron microscopy (SEM). Finally, Eyring’s model was performed to clarify the role of strain rate on activation volume and activation enthalpy of epoxy. The results of tensile tests showed a maximum strength of epoxy–graphene nanocomposite at the graphene wt% of 0.1%. Tensile strength of epoxy obviously improved with increasing strain rate, but tensile strength of epoxy/graphene nanocomposite sample was less sensitive. Fracture micrographs showed that the mirror zone of the fracture surface of epoxy diminished by increasing strain rate or addition of graphene; and final fracture zone also became rougher. Finally, by investigation of the activation enthalpies, it was showed that much higher enthalpy was needed to fracture the nanocomposite sample, as the activation enthalpy changed from 41.54 for neat epoxy to 67.34 kJ mol^?1 for EP–0.1% GNS sample.     

A new colour constancy algorithm based on automatic determination of gray framework parameters using neural network

Colour constancy is defined as the ability to estimate the actual colours of objects in an acquired image disregarding the colour of scene illuminant. Despite large variety of existing methods, no colour constancy algorithm can be considered as universal. Among the methods, the gray framework is one of the best-known and most used approaches. This framework has some parameters that should be set with appropriate values to achieve the best performance for each image. In this article, we propose a neural network-based algorithm that aims to automatically determine the best value of gray framework parameters for each image. It is a multi-level approach that estimates the optimal values for the gray framework parameters based on relevant features extracted from the input image. Experimental results on two popular colour constancy datasets show an acceptable improvement over state-of-the-art methods.