نشریه پژوهش های علوم و صنایع غذایی ایران
سال یازدهم شماره 6 (پیاپی 36، بهمن و اسفند 1394)

The influence of 0-27 g/100g of full-fat soy flour (FFSF), 31-35g/100g of water content and extrusion conditions on the textural characteristics of spaghetti were evaluated. Process was performed with screw speed of 10-40 rpm and water circulating temperature of 35-70°C. This enrichment resulted in significant differences in mechanical strength and cutting parameter. Based on the mixture surface and contour plots, it was found that the optimum textural characteristic of spaghetti could be obtained by addition of 20.6 g/10 g FFSF and 35.0 g/100g water and process in screw speed of 40 rpm and temperature of 35oC.

As traditional extraction methods like Hydrodistillation (HD) and steamdistillation (SD) have long extraction times, some novel extraction methods like microwave-assisted hydrodistillation (MAHD) and ohmic-assisted hydrodistillation (OAHD) are recently introduced. In this study, essential oils of Mentha piperita were extracted by OAHD and MAHD and the results were compared with those of the SD and HD to clarify if these novel procedures have significant effect on antioxidant activities of extracted essential oils. The results showed that OAHD and MAHD are able to reduce extraction time (up to 72%) and also required electrical energy. Furthermore, all extracted essential oils were shown to have approximately same physical properties (relative density and visual color) and antioxidant activity using DPPH and β-carotene bleaching methods. The findings of this study revealed the applicability of using mint essential oil obtained by MAHD and OAHD as a natural antioxidant in food and pharmaceutical products.

In this study, the effects of coating with whey protein concentrate (7.5% w/v) alone and/or in combination with rice bran oil and Zataria multiflora extract on the quality attributes and egg shelf life were observed and analyzed during 4 weeks. Weight loss, Haugh index, yolk index, pH, air cell depth, shell strength and the impact of this coating on the microbial load of the eggs surface were studied at the end of each week. After 4 weeks of storage, the weight loss in all of the treated eggs with whey protein concentrate and 0. gr of rice bran oil was significantly lower than that of the control group(P

In this work, a hybrid GMDH–neural network model was developed in order to predict the moisture content of papaya slices during hot air drying in a cabinet dryer. For this purpose, parameters including drying time, slices thickness and drying temperature were considered as the inputs and the amount of moisture ratio (MR) was estimated as the output. Exactly 50% of the data points were used for training and 50% for testing. In addition, four different mathematical models were fitted to the experimental data and compared with the GMDH model. The determination coefficient (R2) and root mean square error (RMSE) computed for the GMDH model were 0.9960 and 0.0220,and for the best mathematical model (Newton model) were 0.9954 and 0.0230, respectively. Thus, it was deduced that the estimation of moisture content of thin layer papaya fruit slices could be better modeled by a GMDH model than by the mathematical models.

Recently, Subcritical Water Extraction (SWE) has been well known as a green technology for extraction of bioactive compounds from plants. In this study, Subcritical water extraction, ultrasound assisted extraction (UAE) and shaker solvent extraction (SSE) were compared for extraction of phenolic compounds from fig (Ficuscarica) pulp and skin. Antioxidant activity of the extracts was evaluated using DPPH radical scavenging, reducing power and rancimat tests. Subcritical waterhad the highest ability for extraction of total phenolic content (65.89±0.21 and 80.79±0.09 mg of gallic acid equivalents per gram of extract respectively) and flavonoid compounds (7.51±0.33 and 10.1±1.02 mg of quercetinequivalents per gram of extract, respectively)from both pulp and skin.The lowest IC50 in DPPH radical scavenging and reducing power tests were related to SWE of skin extract of fig. Furthermore, in extraction of total phenol and flavonoid compounds, subcritical water extraction showed to be a more suitable method than other solvent extraction methods, both in pulp and skin.

Thermal conductivity is an important property of juices in the prediction of heat- and mass-transfer coefficients and in the design of heat- and mass-transfer equipment for the fruit juice industry. An artificial neural network (ANN) was developed to predict thermal conductivity of pear juice. Temperature and concentration were input variables. Thermal conductivity of juices was outputs. The optimal ANN model consisted 2 hidden layers with 5 neurons in first hidden layer and the second one has only one neuron. The ANN model was able to predict thermal conductivity values which closely matched the experimental values by providing lowest mean square error (R2=0.999) compared to conventional and multivariable regression models. However this method also improves the problem of determining the hidden structure of the neural network layer by trial and error. It can be incorporated in heat transfer calculations during juices processing where temperature and concentration dependent thermal conductivity values are required.