The aim of this study was to investigate the effect of ultrasound power at 40 to 80 percent (equal to 232-464 W), extraction temperature (50 to 80 °C) and extraction time (10 to 30 min) on the soluble polysaccharide extraction from the Pleurotus ostreatus. The response surface methodology using Box-Behnken design (with three variables, three levels and 5 replications at central point) was applied to optimize extraction conditions and evaluation of the effects of main factors and their interactions. Antioxidant activity (scavenging ability of OH and DPPH) of extracted polysaccharide were also evaluated. Optimization of polysaccharide extraction yield using response surface methodology indicated that combination of the ultrasonic power of 58.06 percent (~337 W), extraction temperature of 65.15 °C and extraction time of 21.72 min resulted in maximum extraction yield (17.71%). Among three independent variables, ultrasonic power had the highest and temperature had the lowest impact on the rate of extraction. The results from antioxidant activity evaluation showed that even though extracted polysaccharide had lower absorbance capacity of the free radicals in comparison with control samples (ascorbic acid and BHT), but it revealed an acceptable antioxidant property.
The present study was conducted to investigating the effect of Persian gum (PG) and microbial transglutaminase (MTGase) on sensorial, color and microbial characteristics of ultrafiltrated semi-fat white cheese during 60 days of cold storage. In order to produce semi-fat cheeses, PG was used at three levels of 0, 0.25, and 0.5% and MTGase enzyme at three levels of 0, 0.5 and 1 unit/g of protein. The results revealed that the treatment of cheese samples with PG and MTGase enzyme had a positive effect on the sensory and quality characteristics of the product. In general, the cheese sample containing 0.5% PG and 0.5 units of MTGase enzyme attained the highest sensorial scores. Based on panelists’ preference, during the storage time, aroma and texture scores increased while color and appearance attributes decreased. The results obtained from the analysis of color values revealed that the lightness (L*) of cheeses increased with the addition of PG and MTGase enzyme treatment and decreased with the passage of storage time. Unlike the lightness, PG and MTGase enzyme had no significant effect on a* (red-green) and b* (yellow-blue) values of the experimented cheese samples. The results obtained from the microbial evaluation showed that the addition of PG increased the viability of lactic acid bacteria (LAB), but it had no effect on the count of mold and yeasts. On the other hand, increasing the concentration of the enzyme decreased the growth and survival of the studied microorganisms. The results of this study showed that PG can be used as a fat substitute along with MTGase enzyme to produce ultrafiltrated low-fat white cheese with favorable technological and sensory characteristics comparable to high-fat cheese varieties, and the best sample of ultrafiltrated semi-fat cheese is obtained using a treatment containing 0.5% PG and 0.5 unit of MTGase enzyme.
The problem of determining the prestressing force in the tendons of prestressed concrete structures and monitoring the non-exceedance of prestressing drops is an issue that has been addressed by many researchers over the past decades and has provided methods in this field. Today, pre-installation sensors are installed in important prestressed concrete structures to monitor prestressing loss. However, due to the unpredictability of such equipment in older structures, monitoring of these forces requires destructive or non-destructive testing but is inaccurate. Therefore, in this paper, a method is presented that without the need for these sensors and destructive tests, only by measuring static displacement, is able to detect the amount of prestressing loss in the cross-sectional tendons of a prestressed concrete beam. In this regard, an algorithm in the Python program environment based on genetic algorithm as well as modeling in the finite element analysis program is provided. The numerical example presented in this research shows that the proposed algorithm detects the values of prestressing loss with good accuracy even in spite of 10% of the intentional error due to measurement. In recent years, the use of prestressing methods has become much simpler and more effective, and its materials have been optimized. Today, a high percentage of structures under construction worldwide are built using this technology, and the advance has found wide applications in the construction of office buildings, residential, commercial, parking lots, sports stadiums, concrete tanks and special structures such as piers. Therefore, in recent years, for long-term monitoring of prefabricated structures, equipment and sensors sensitive to force drop, such as fiber optic sensors and FBG sensors in the construction phase are predicted and installed in the desired locations. [13] However, since the above equipment requires a lot of money and it is not possible to use them in old structures, the need for a technique that shows the amount and location of force reduction in all tendons without using them remains. Therefore, in this paper, a method is presented that, while using the simplest tools, provides the most accurate results only by measuring static displacements under the effect of various loading scenarios and using an artificial intelligence algorithm based on genetic algorithm. The proposed method is based on computer analysis and comparison of the results of two prestressed concrete beams with the same geometry, loading and arrangement of tendons. First, a specific prestressing beam is modeled in the SAP2000 analysis program and the desired prestressing forces are applied to it, and then these forces are reduced in some of the studied tendons. This deliberate change in prestressing values is considered as failure and the technique presented in this mapping tries to discover the extent and location of failure of this beam. In other words, this paper is the determination of the amount of prestressing force in prestressed concrete beams in which force measuring sensors are not predicted without the need for destructive testing and only by measuring the static displacement under load. In the form of a numerical example on a prestressed concrete beam consisting of 6 steel tendons and using a genetic algorithm, it was shown that the displacement is a function of the amount of prestressing and its location and amount of reduction by the technique used. It was correctly detected with 93% accuracy when 10% of the deliberate error due to displacement field measurement was applied. As a suggestion for future work, this research will be able to be developed in the simultaneous diagnosis of prestressing reduction and beam concrete failure.