s. m. a. razavi

In this research, sage seed gum was extracted using the dry method (friction-based) and the extraction conditions was optimized by the response surface method and using the box-Behnken design. Dry extraction method was used to achieve an economical, inexpensive, fast, eco-friendly (green technology), continuous, easy usage and automated method. The independent variables in this research were rotor speed (750, 825, 900 rpm), angle of friction (0, 10, 20 degree) and friction distance (1.5, 2, 2.5 mm). The effects of these variables on extraction yield, protein content and apparent viscosity (at a shear rate 46 1/s) were investigated. The results showed that quadratic polynomial models adequately explained the data variation and represented the actual relationship between independent variables and responses. The optimum conditions with maximum extraction yield and apparent viscosity and minimum protein content were achieved when the rotor speed, angle of friction and friction distance were 900 rpm, 6.9 degree, 2.2 mm, respectively. At the optimized conditions, the extraction yield, protein content and apparent viscosity were obtained 10.61%, 7.02%, and 112 mPa.s, respectively

In this study, the effects of fat content (0.2-5.8%) and temperature (5-35°C) on viscosity, density, color, and the friction pressure drop per unit length of pipe flow of camel milk were investigated by using response surface methodology (RSM) based on the central composite design (CCD). The results showed that increasing the fat content decreased the density of samples with high-fat content, while increasing the temperature decreased the density, but it had no effect on samples with 0.2% fat. The results of the viscosity analysis also showed that increasing the temperature decreased the viscosity of samples containing high-fat. Also, the viscosity of the samples increased with increasing the fat content, although the increase in viscosity by increase in fat at low temperatures was much higher than that at high temperatures. The results of color measurement also showed that increasing in fat content increased only b* parameter and temperature had no significant effect on other color parameters. According to the results of ANOVA, none of the linear, quadratic and interaction effects of fat and temperature on the friction pressure drop per unit length of pipe were significant (p> 0.05).

Nowadays, demands for hydrocolloids which improve the rheological properties of foods as well as retain their properties under the influence of food additives have increased. In this study, dilute solution properties were employed to understand the molecular and conformational properties of Alyssum homolocarpum seed gum (AHSG), in presence of sucrose and lactose. The model of Tanglertpaibul & Rao was selected as the best model for the estimation of the intrinsic viscosity. It was shown that except for water, the solutions of sucrose and lactose are poor solvents for AHSG as indicated by a decrease in intrinsic viscosity, swollen specific volume, shape function, and coil dimensions. As the sucrose and lactose concentrations increased, the coil radius decreased. The reduction in the shape and swollen volume parameters in the presence of sucrose and lactose as compared to the sugar-free solution indicated the negative effect of the opted sugars on the molecular volume of the gum. Evaluations of the dilute solution properties of the gum in sucrose and lactose solutions revealed that the existence of a conformation tending to ellipsoidal shape and the probability of the conformation of random coil with no molecular entanglements in AHSG solutions.

In this study, a mixture of milk protein concentrate, whey protein concentrate, skim milk powder, soymilk, margarine, butter and water was used for production of recombined UF-Feta cheese analogue. Variables were milk protein concentrate (8%, 9%, 10%), whey protein concentrate (0%, 1.5%, 3%), soymilk (5%, 10%, 15%) and margarine (0%, 5%, 10%). Textural properties of Samples were analyzed 3 days post-manufacture. The central composite design was employed and the results were modeled and analyzed using response surface methodology. Coefficients of determination, R2, of fitted regression models for different variables were varied in the range of 89.59-97.80 and the lack-of-fit was not significant for all responses at 95%. Hence, the models for all the response variables were highly adequate. The results showed that the optimum processing conditions for producing cheese with suitable hardness and cohesiveness and lowest adhesiveness were: 9.13% milk protein concentrate, 3% whey protein concentrate, 15% soymilk and 7.65% margarine.

Pistachio nut is one of the most delicious and nutritious nuts in the world and it is being used as a salted and roasted product or as an ingredient in snacks, ice cream, desserts, etc. (Maghsudi, 2010; Kashaninejad et al. 2006). Roasting is one of the most important food processes which provides useful attributes to the product. One of the objectives of nut roasting is to alter and significantly enhance the flavor, texture, color and appearance of the product (Ozdemir, 2001). In recent years, spectral imaging techniques (i.e. hyperspectral and multispectral imaging) have emerged as powerful tools for safequality inspection of various agricultural commodities (Gowen et al., 2007). The objectives of this study were to apply reflectance hyperspectral imaging for non-destructive determination of moisture content and hardness of pistachio kernels roasted in different conditions.

Dried O’hadi pistachio nuts were supplied from a local market in Mashhad. Pistachio nuts were soaked in 5L of 20% salt solution for 20min (Goktas Seyhan, 2003). For roasting process, three temperatures (90, 120 and 150°C), three times (20, 35 and 50 min) and three air velocities (0.5, 1.5 and 2.5 m s-1) were applied. The moisture content of pistachio kernels was measured in triplicate using oven drying (3 gr samples at 105 °C for 12 hours). Uniaxial compression test by a 35mm diameter plastic cylinder, was made on the pistachio kernels, which were mounted on a platform. Samples were compressed at a depth of 2mm and speed of 30 mm min-1. A hyperspectral imaging system in the Vis-NIR range (400-1000 nm) was employed. The spectral pre-processing techniques: first derivative and second derivative, median filter, Savitzkye-Golay, wavelet, multiplicative scatter correction (MSC) and standard normal variate transformation (SNV) were used. To make models at PLSR and ANN methods, ParLeS software and Matlab R2009a were used, respectively. The coefficient of determination (R2), the root mean square error of prediction (RMSEP) and the ratio of the standard deviation of the response variable to RMSEP (known as relative performance determinant (RPD)) were calculated.

Interpretation of hyperspectral data: The results showed that the spectra of the shell, the whole kernel and the internal part of the kernel have different patterns. The internal part of thekernel had 2 peaks at 630 nm and 690 nm, while the shell and the whole kernel had 1 peak at 670 nm and 720 nm, respectively and the peak of the whole kernel was sharper than that of the shell. The highest and lowest intensities were for the internal part of the kernel and the whole kernel, respectively. The spectral slope of the internal part is higher than that of the shell and the whole kernel at 500-700 nm. The effect of different pre-processing techniques and analysis on prediction of pistachio kernels properties: In the absence of pre-processing techniques, low correlation coefficients were observed for prediction of moisture content and hardness. However, with the use of pre-processing techniques, in some models, correlation coefficient and RPD increased and the RMSEP decreased. The results revealed that ANN models would predict moisture content and textural characteristics of roasted pistachio kernels better than PLSR models. Moisture content: ANN models can predict moisture content of roasted pistachio kernels better than PLSR models. In total, PLSR models showed low RPD and R2. For all samples, RPD was lower than 1.5, indicating that the developed models do not give an accurate prediction for moisture content. The best results with ANN method were achieved using a combination of SNV, wavelet and D1 for predicting moisture content with R2 =0.907 and RMSEP=0.179. Hardness: The results indicated that ANN models can predict the hardness better than PLSR models. The best results with PLSR models were achieved using a combination of SNV, wavelet and D1 with R2= 0.643, RMSEP=10.78, RDP= 1.48 and 2 PLSR factors. However, due to high RMSEP and low R2 and RPD, it can be mentioned that prediction of hardness values with ANN model was not sufficiently desirable. However it was better than the PLSR models. The best results with ANN models were achieved using a combination of SNV, wavelet and D2 with R2=0.876 and RMSEP=5.216.

The results of this study showed that employing pre-processing methods causesa decrease in prediction error and improves the quality of the models. ANN models could predict moisture content and hardness of roasted pistachio kernels better than the PLSR models.

In this study, artificial neural network (ANN) model was used to predict the average permeate flux and NaCl rejection in nanofiltration process of sugar industry decolorization columns waste water. Nanofiltration process was performed at three temperatures 30, 4 and 50°C, at three pressures of 1, 1.5 and 2 MPa, at three concentration levels 60, 80 and 100 g/l and at two pH levels 8 and 9. In order to predict the permeate flux and NaCl rejection multi-layer perceptron neural network with 4 inputs and 2 outputs were used. The results showed a network with 9 neurons in hidden layer and using a hyperbolic tangent transfer function and the Levenberg–Marquardt (LM) optimization technique and 30%-30%-40% data for training/ testing/ validating process can be well predict the permeate flux (0.98) and NaCl rejection (0.88) in the nanofiltration of decolorization column wastewater. Sensitivity analysis results by optimum ANN showed the pressure was the most sensitive factor for prediction of both flux and rejection by the selected ANN.

In this paper, the effect of two domestic seeds hydrocolloids (Basil seed and Sage seed gums) and one commercial hydrocolloid (pectin) on characteristics of non-fat concentrated yoghurt was studied. For this purpose, effect of different concentrations of these gums (0.05, 0.1, 0.15 and 0.2%) on physicochemical and sensory properties after 1 and 7 days were investigated and compared with control sample (without gum). Physicochemical characteristics were included total solids, syneresis, apparent viscosity and softness of concentrated yoghurt. Flavor, odor, color and mouth feeling were evaluated as sensory properties. With increasing the concentration of gums, total solids, syneresis, apparent viscosity and softness of samples decreased. Yogurts containing 0.05% gum was acceptable by panelists. The highest apparent viscosity and sensory acceptance were seen in samples containing sage seed gum, whereas samples containing pectin had the highest total solids and firmness. The apparent viscosity of samples decreased after 7 days storage, whereas the syneresis and softness increased, and sensory properties improved.

In this study, the effect of transmembrane pressure (TMP) on flux decline and protein rejection due to concentration polarization and fouling (adsorption) resistances during ultrafiltration of reconstituted skimmilk was investigated. UF experiments were carried out using a pilot plant unit equipped with spiral wound module and polysulfonamide UF membrane. A three-stage experimental strategy based on a resistance-in-series model (boundary layer-adsorption) was used to seperately determine the hydraulic membrane resistance, concentration polarization and fouling resistances. The results showed that increasing TMP had no effect on initial flux reduction (Jrt), but flux decline in each TMP was greatly due to concentration polarization, and fouling has a small role in flux decline, whereas dynamic response of flux decline proved that increasing Jrt during operation is due to fouling. The influence of TMP on resistances showed that total hydraulic resistance and reversible fouling resistance are increased with increasing TMP, but intrinsic membrane resistance and irreversible fouling resistance did not change. Increasing the total hydraulic resistance with time resulted in increasing both reversible and irreversible fouling resistances. Increasing TMP or operation time had no significant effect on protein rejection, but only led to an increase about 1-4 percent.