scholarly journals Optimization and evaluation of textural properties of ultra-filtrated low-fat cheese containing galactomannan and Novagel gum

Author(s):  
Sharmineh Sharafi ◽  
Leila Nateghi ◽  
Orang Eyvazzade ◽  
Maryam Ebrahimi Taj Abadi

The general aim of this research was to optimise textural properties and to evaluate the possibility of producing ultra-filtrated low-fat cheese (7-9 % (w/w)), containing various concentrations of galactomannan and Novagel (0.1-0.5 % w/w), and assessing textural properties of produced low-fat cheeses and comparing them with full fat ones. According to the results, reducing fat implies increasing the hardness, cohesiveness, gumminess and chewiness of the tested samples. On the other hand, adding galactomannan gum and Novagel, and increasing their concentration, implies reducing all of the above mentioned textural properties. According to the results, increasing the amount of fat and using galactomannan and Novagel gum, implies increasing the adhesiveness and springiness of the tested treatments. The results showed that textural properties including hardness, adhesiveness, cohesiveness, gumminess and chewiness, of sample containing 9 % (w/w) fat, 0.5 % (w/w) galactomannan, and 0.3 % (w/w) Novagel were not of significantly different from the control sample and was selected as the superior sample. Multiple optimization of the low-fat cheeses textural properties via Response Surface Method (RSM) software showed that the treatment containing 9 % (w/w) fat, 0.1 % (w/w) Novagel and 0.46% (w/w) galactomannan fulfils 84 % of desirable properties of a full fat cheese.

2014 ◽  
Vol 134 (9) ◽  
pp. 1293-1298
Author(s):  
Toshiya Kaihara ◽  
Nobutada Fuji ◽  
Tomomi Nonaka ◽  
Yuma Tomoi

Materials ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 3552 ◽  
Author(s):  
Chun-Yi Zhang ◽  
Jing-Shan Wei ◽  
Ze Wang ◽  
Zhe-Shan Yuan ◽  
Cheng-Wei Fei ◽  
...  

To reveal the effect of high-temperature creep on the blade-tip radial running clearance of aeroengine high-pressure turbines, a distributed collaborative generalized regression extremum neural network is proposed by absorbing the heuristic thoughts of distributed collaborative response surface method and the generalized extremum neural network, in order to improve the reliability analysis of blade-tip clearance with creep behavior in terms of modeling precision and simulation efficiency. In this method, the generalized extremum neural network was used to handle the transients by simplifying the response process as one extremum and to address the strong nonlinearity by means of its nonlinear mapping ability. The distributed collaborative response surface method was applied to handle multi-object multi-discipline analysis, by decomposing one “big” model with hyperparameters and high nonlinearity into a series of “small” sub-models with few parameters and low nonlinearity. Based on the developed method, the blade-tip clearance reliability analysis of an aeroengine high-pressure turbine was performed subject to the creep behaviors of structural materials, by considering the randomness of influencing parameters such as gas temperature, rotational speed, material parameters, convective heat transfer coefficient, and so forth. It was found that the reliability degree of the clearance is 0.9909 when the allowable value is 2.2 mm, and the creep deformation of the clearance presents a normal distribution with a mean of 1.9829 mm and a standard deviation of 0.07539 mm. Based on a comparison of the methods, it is demonstrated that the proposed method requires a computing time of 1.201 s and has a computational accuracy of 99.929% over 104 simulations, which are improvements of 70.5% and 1.23%, respectively, relative to the distributed collaborative response surface method. Meanwhile, the high efficiency and high precision of the presented approach become more obvious with the increasing simulations. The efforts of this study provide a promising approach to improve the dynamic reliability analysis of complex structures.


2021 ◽  
Author(s):  
Alfikri Khair ◽  
Haryudini A. Putri ◽  
Suprapto Suprapto ◽  
Yatim L. Ni’mah

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