mohammad khalilian movahhed

Efforts have always been made to protect valuable compounds of medicine, food and aromatics materials that are highly sensitive to environmental conditions by the encapsulation method. encapsulation of flavors, in addition to its protection, allows the aromatic substance to be released in a long time, and the time and place of its release can be controlled. To design these protection systems requires detailed information on encapsulation and release methods, the nature of walls and aromatic materials (Gunning et al.,1999). For encapsulation of sensitive compounds such as lipophilic materials, it is necessary to produce an emulsion of the desired substance in wall materials such as proteins, polysaccharides or a mixture of them. The important factors in encapsulation are the molecular weight, chemical properties and polarity of the core materials, the properties of the materials of the walls, and finally, the methods used to produce microcapsules. (Jafari et al., 2008).The aim of this study was to produce and evaluate the properties of two and six layer multilayer microcapsules containing limonene using soy protein isolate and starch modified by spray drying. The release of encapsulated limonene was investigated under artificial oral conditions under different stress conditions. The results of this study can be used to predict the release rate of the encapsulated flavors and their release conditions.

Solution preparation: The solution of SPI (0-3%) was prepared by methods of Huang et al. (2012). The OSA starch stock solution (0-2%) was prepared by methods of Nilsson and Bergens (2007).Emulsion’s preparation: the primary emulsion of the optimum SPI and secondary emulsion of optimum OSA starch concentration prepared by the method of Noshad et al (2015).Microcapsule production: To prepare the Microcapsules, a laboratory spray dryer was used. 180±5 ᵒC, inlet air temperature, 25 (ml/min) feed rate, and 90±10 ᵒC outlet air temperature were used. Six layer microcapsules was also prepared in the same conditions (Ansarifar et al., 2017)The micro structure, morphology and release of limonene were evaluated and finally by Zero order, First order, Higuchi, and Korsemeyer- peppas models were used to the fitting of experimental data.Limonene release: To investigate the release of the encapsulated limonene, the release of these microcapsules (two and six layer) at 37 ° C and pH = 6.8, as well as frequent chewing (0, 50 and 100 rpm) were examined. For the apply of shear stress, an oral simulator was designed and developed by the Department of Food Science and Technology of Ferdowsi University of Mashhad was used.

Results and Discusion:

The results of particle size changes of the initial emulsion formed with different levels of soy protein isolate showed that the particle size decreased with increasing the concentration of this protein to 1.5% and then it was increased. The results of zeta potential showed that with increasing the concentration of soy protein isolate to 1.5%, the zeta potential of the samples increased and with more than 1.5%, it did not have much effect on the zeta potential of the samples, which indicates that concentrate of 1.5% soy protein isolate has a good ability to cover surface of limonene particles. Similarly, 1.2% of OSA starch was determined for the secondary layer.SEM images of the microcapsules showed that in the two-layer wall microcapsules have cavities, cracks and shrinkage. In the starting of drying, the rate of moisture lost is high and on the other hand, the wall is not strong enough to withstand the stresses caused by the exit of moisture from the walls, so the microcapsule has cavities. In six-layer microcapsules, a smooth, non-cracked surface was observed, which can be attributed to the wall strength due to the increase in the number of layers. Fourier transform infrared spectroscopic (FTIR) test showed that the outer surface of the microcapsules was covered by OSA starch in two and six layer microcapsules.The release profile of encapsulated limonene showed that the release rate in two layer samples was faster than six layer samples. Also, with increasing shear rate, the amount of release increased. The results of experimental models fitting showed that the first-order model had the best description for releasing limonene from two- and six-layer samples in different conditions. Calculation of diffusion coefficient showed that six-layer microcapsules have a lower diffusion coefficient than two-layer microcapsules, which leads to a decrease in the release rate of limonene.

 The results of this study showed that the layer-by-layer method could be used to produce limonene microcapsules. Soy protein isolate and modified starch can cover limonene droplets well. SEM images showed that the structure of six-layer microcapsules is free of cracks and cavities and has a more uniform surface than two-layer microcapsules. To investigate the mechanism of limonene release from two- and six-layer microcapsules, different kinetic models were used to fit the experimental release data. The results showed that the release of these microcapsules occurred based on the diffusion mechanism and Fick's law, which is the main mechanism of mass transfer in the release process. Also, the results showed that the six-layer microcapsules had a lower diffusion coefficient than the two-layer microcapsules and the release rate was lower in the two-layer microcapsule; This is due to the repetitive coating of soy protein isolate and modified starch around the microcapsules and the increase in wall thickness.

Saffron is one of the most expensive spices in the world. Saffron is a spice that is widely cheated. The development of techniques based on simple, inexpensive, appropriate and fast tools in the food industry is essential for detecting adulteration such as saffron adulterated. In the present study, the combination of image processing and Support vector machine (SVM) method has been used for fast and non-destructive evaluation of distinguishing authentic saffron from adulterated saffron. After preparing images from pure and counterfeit saffron and separate stigmas, the images entered the pre-processing stages and finally, statistical features related to the texture of the images and morphological features including 105 features were extracted. In order to increase the speed and accuracy of classification, PCA principal component analysis method was used to reduce the properties of the feature matrix. Also, the images were classified into two classes using different SVM kernel functions. Also, the images were classified into two classes using different SVM kernel functions. Then statistical indicators such as accuracy, precision, sensitivity, specificity and AUC were calculated to evaluate the classification. The values of these indices for classification with SVM cubic kernel for authentic saffron were 97, 98, 99, 93 and 97%, and for adulterated saffron, 97, 93, 83, 97.5 and 97% were obtained, respectively. The results of this classification showed that this system, as an intelligent, fast, non-destructive and accurate method, has the ability to distinguish the authentic saffron from adulterated saffron.

Kichi is a local sweet bread that is cooked in the west of Iran, especially in Chaharmahal and Bakhtiari Province. This product is traditionally produced and has a low shelf life and hard texture. Therefore, the aim of this study was to investigate the effect of xanthan gum (0, 0.1, 0.3 and 0.5%) to improve the quality of Kichi sweet bread. Also mean size area, porosity, fractal dimension, and color parameters of Kichi bread were evaluated. The results showed that addition of xanthan led to improving WHC. The results of the texture analysis showed that softer samples were obtained in the low concentrations of xanthan gum (0.1% based on flour) and gumminess in this sample was significantly less than other samples (p < /em><0.050). Also, the porosity of the product in the low concentrations of xanthan (0.1%) was similar to the control, but in the high concentration of xanthan gum, the porosity of the product reduced. On the other hand, the results showed that the addition of xanthan gum did not affect the fractal dimension of the product texture. In the case of product color, the xanthan had a significant effect on the L* and b* values of the texture. Finally, it can be stated that using 0.1% of xanthan gum in the formulation of this local bread led to the production of a quality product regarding color and texture. The sensory evaluation results of the samples showed that the addition of xanthan improves the sensory properties of the samples. In general, samples containing 0.1% xanthan had better texture and sensory properties.

In this study, Barbari flat bread was prepared by two methods, including part-baking and conventional baking and then stored at 4 °C for 7 days. Part-baking was done for 4, 6 or 8 minutes and conventional bread were prepared for 10 minutes (control). The samples were evaluated for the crust color, porosity and mean particle size by using ImageJ software during the storage time. Also, image texture features of samples were extracted using MATLAB software. Before acquiring images, the baking time of Barbari part-baked bread was completed to the baking time of control bread (10 minutes). Crust color of part-baked samples did not significantly change during 7 days, but the lightness of control bread decreased significantly. In addition, because of less porous and more compact texture of control bread and also, stronger changes in their image texture characteristics compared to part-baked bread during storage time, part-baking was a successful method for production of Barbari flat bread. Part-baked breads with different initial baking times were not completely similar. On the other hand, the results showed that the part-baked bread with different initial baking times were not similar in terms of quality characteristics. By comparing three quality features of the sample images after the second baking, the part-baked Barbari bread with initial baking for 6 minutes had higher quality and stability than other treatments.