The Effect of Drinking Yoghurt Containing Free and Microencapsulated Probiotic Bacteria on Changes of the Population of These Bacteria in the Digestive System

Background & Aims: Probiotic bacteria have beneficial effects on host's health. However, one of the most important reasons which affect the probiotic activity of a microorganism is its survival during the gut transit. Microencapsulation techniques could be applied to bacteria to improve this parameter. Methods: In this study, feces of 60 healthy volunteers were analyzed during 28-day test period to assess changes of probiotic bacteria. Participants were divided into equal 4 groups; group 1 did not receive probiotic drinking yoghurt (control); group 2 received probiotic drinking yoghurt containing free Lactobacillus acidophilus and Bifidobacterium animalis, subspecies lactis; group 3 recieved the same strains microencapsulated with sodium alginate/resistant starch; and group 4 received probiotic drinking yoghurt containing microencapsulated probiotic bacteria with sodium alginate/chitosan. Results: A significant increase was recorded in the population of lactobacilli and bifidobacteria in the feces of participant in three groups at the end of the treatment compared with control group (P < 0.05 for all), confirming the ability of the 2 strains to colonize the human gut, either in a gastroprotected form or not. Participants treated with the microencapsulated bacteria reported more viability than those received not encapsulated strains. Feces of group 3 that received drinking yoghurt containing encapsulated probiotic bacteria with alginate/resistant starch had higher amount of probiotic bacterial populations, 1.3 ± 0.26 × 107 and 2.4 ± 0.37 × 109 cfu/g Lactobacillus acidophilus and Bifidobacterium animalis subs lactis, respectively. Conclusion: Consumption of the drinking yoghurts containing probiotic bacteria increased the Lactobacillus acidophilus and Bifidobacterium animalis, subspecies lactis, contents of the feces and encapsulation process improved stability of probiotic bacteria

Application of the Response Surface Method in the Analysis of Ohmic Heating Process Performance in Sour Orange Juice Processing

Three voltage gradients (8.38, 10.83, and 13.33 V cm-1) and three weight loss percentages (10, 20, and 30 percent) were examined; also the system performance coefficient, input current, heating process duration, power consumption and electrical conductivity investigated. The response surface method was also used for modeling s and optimization. For the response surface method, weight loss percentage and voltage gradient were selected as independent variables; and factors  system performance coefficient, heating process duration, input current, power consumption and electrical conductivity were selected as responses. According to results, all obtained models were significant for responses factors, but the voltage gradient and weight loss percentage were insignificant for all factors except the electrical conductivity and power consumption. The best model was a quadratic model against interaction for the system performance coefficient, input current and power consumption; and the linear model against mean was the best model for electrical conductivity and heating time

Modeling of moisture loss kinetics and color changes in the surface of lemon slice during the combined infrared-vacuum drying

Color is one of the most important appearance attributes of food materials, since it influences consumer acceptability. In this study the effects of combined infrared-vacuum drying on the drying kinetics, moisture diffusivity, surface changes (shrinking) and color changes kinetics of lemon slices were investigated. Both the infrared lamp power and vacuum pressure influenced the drying time of lemon slices. The regression results showed that the quadratic model satisfactorily described the drying behavior of lemon slices with highest R value and lowest SE values. The effective moisture diffusivity increased from 2.92 × 10−10 and 1.58 × 10−9 m2/s when the infrared lamp power was increased from 300 to 400 W. The colour parameters L∗ (lightness), a∗ (redness/greenness), b∗ (yellowness/blueness), and ΔE (total colour difference) were used to estimate colour changes during drying. L∗, a∗ and b∗ values of dried lemon increased during drying. The rise in infrared power has a negative effect on the ΔE and with the increase of infrared radiation power it was increased. Different kinetic models were used to fit the experimental data and the results revealed that the power model was the most suitable to describe the color change intensity (ΔE). Keywords: Colour parameters, Image processing, Infrared drying, Kinetic, Lemo

Optimizing Physiochemical and Sensory Properties of Infrared-Hot Air Roasted Sunflower Kernels Using Response Surface Methodology

Roasting sunflower kernels is a key process in production of nuts. In this study, the effect of roasting conditions, including hot air temperature (120–160°C), infrared (IR) power (400–600 W) and roasting time (3–10 min) on energy and specific energy consumption, color parameters (L∗, a∗, b∗, ΔE, BI, SI, WI, and h°), texture, moisture content, chemical properties (pH and total phenolic contents, peroxide value (PV), and sensory properties of sunflower kernel were investigated. In addition, the best models for the responses were obtained, and the proper roasting conditions were determined using response surface methodology (RSM). A quadratic model was proposed for color change (L∗, ΔE, SI, and WI), moisture and total phenol contents, linear relation for a∗, b∗, h°, and 2FI for BI, texture, PV, and pH. Roasting at 425.7 W IR power and 124.3°C for 3.7 min was found to be convenient or proper roasting conditions

Analyzing Greenhouse Gas Emissions in the Ohmic Heating Drying Process of Sour Orange Juice

An ohmic heating device was fabricated. For the heating process in three input voltage gradients and three percent weight loss of the sample were selected. During the thermal process, the power consumption was calculated and then using the ratios of pollution in power plants, the amount of Nox, SO2, and CO2 in three Steam, Gas turbine and Combine cycle power plants was calculated. All experiments were performed in three replications using CRD statistical design and the results were analyzed using SAS software. The highest amount of Nox was first in the Gas turbine plant and then in the Combine cycle power plant and Steam power plant, respectively. The amount of SO2 in Heavy gas used in the Steam power plant is more than the other two power plants, and then the highest amount of SO2 is obtained in Gas turbine power plant and finally in Combine cycle power plant. Also, the amount of CO2 in the Combine cycle power plant was less than the other two types of power plants. In general, when the factors of ohmic voltage and weight loss percentage are considered for the ohmic process, natural gas and Combine cycle power plant create the best possible state

A study on the energy and exergy of Ohmic heating (OH) process of sour orange juice using an artificial neural network (ANN) and response surface methodology (RSM)

The nonmodern statistical methods are often unusable for modeling complex and nonlinear calculations. Therefore, the present research modeled and investigated the energy and exergy of the ohmic heating process using an artificial neural network and response surface method (RSM). The radial basis function (RBF) and the multi-layer perceptron (MLP) networks were used for modeling using sigmoid, linear, and hyperbolic tangent activation functions. The input consisted of voltage gradient; weight loss percentage, duration ohmic, Input flow, Power consumption, electrical conductivity and system performance coefficient and the output included the energy efficiency, exergy efficiency, exergy loss, and improvement potential. The response surface method was also used to predict the data. According to the result, the best prediction amount for energy and exergy efficiencies, exergy loss and improvement potential were in RBF network by sigmoid activation function and after this network, RSM had the best amount for energy efficiency, Also for exergy efficiencies, exergy loss and improvement potential obtained acceptable results in MLP network by a linear activation function. The worst amount was at MLP network by tangent hyperbolic. In general, the neural network can have more ability than the response surface method

Rheological Characteristics of Soluble Cress Seed Mucilage and β-Lactoglobulin Complexes with Salts Addition: Rheological Evidence of Structural Rearrangement

Functional, physicochemical, and rheological properties of protein–polysaccharide complexes are remarkably under the influence of the quality of solvent or cosolute in a food system. Here, a comprehensive description of the rheological properties and microstructural peculiarities of cress seed mucilage (CSM)-β-lactoglobulin (Blg) complexes are discussed in the presence of CaCl2 (2–10 mM), (CSM–Blg–Ca), and NaCl (10–100 mM) (CSM–Blg–Na). Our results on steady-flow and oscillatory measurements indicated that shear thinning properties can be fitted well by the Herschel–Bulkley model and by the formation of highly interconnected gel structures in the complexes, respectively. Analyzing the rheological and structural features simultaneously led to an understanding that formations of extra junctions and the rearrangement of the particles in the CSM–Blg–Ca could enhance elasticity and viscosity, as compared with the effect of CSM–Blg complex without salts. NaCl reduced the viscosity and dynamic rheological properties and intrinsic viscosity through the salt screening effect and dissociation of structure. Moreover, the compatibility and homogeneity of complexes were approved by dynamic rheometry based on the Cole–Cole plot supported by intrinsic viscosity and molecular parameters such as stiffness. The results outlined the importance of rheological properties as criteria for investigations that determine the strength of interaction while facilitating the fabrication of new structures in salt-containing foods that incorporate protein–polysaccharide complexes

Textural, color and sensory attributes of peanut kernels as affected by infrared roasting method

Roasting is one of the widespread methods for processing of nuts that significantly enhances the flavor, color, texture and appearance of products. In this research, the response surface methodology was used to optimize the roasting process over a range of infrared power (250–450 W) and roasting times (10–30 min). The moisture content, color parameters (L*, a*, b *and total color difference (ΔE)), textural characteristics (hardness and compressive energy), energy consumption and sensory evaluation (total acceptation) were determined after roasting and modeled by response surface methodology (RSM). Increasing in roasting IR power and time caused increasing in the energy consumption, a*, b* and ΔE values. The L* value, moisture content, hardness and compressive energy also decreased with increasing roasting IR power and time. The full quadratic model developed by RSM adequately described the changes in the b* value, moisture content and hardness. The result of RSM analysis showed that all color and textural parameters could be used to monitor the roasting of peanut kernels in an infrared roaster, while application of RSM for developing a predictive model that described the total acceptance changes during roasting of peanut kernels was not successful. To obtain the desired color, moisture, texture and acceptation, the optimum roasting range for production of snack was determined as 370 W for 20 min. Keywords: Peanut kernels, IR roasting, RSM, Energy consumptio