scholarly journals The effect of composition, microfluidization and process parameters on formation of oleogels for ice cream applications

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
E. Silva-Avellaneda ◽  
K. Bauer-Estrada ◽  
R. E. Prieto-Correa ◽  
M. X. Quintanilla-Carvajal

AbstractThe use of oleogels is an innovative and economical option for the technological development of some food products, among them ice creams. The aim of this study was to establish the best processing conditions to obtain an emulsion which form oleogels with the lowest ζ-potential and average droplet size (ADS) for use as ice cream base. Using surface response methodology (SRM), the effects of three numerical factors (microfluidization pressure, oil and whey protein concentration, WP) and four categorical factors (oil type, temperature, surfactant, and type of WP) on formation of emulsions were assessed. The response variables were ζ, ADS, polydispersity index (PDI), viscosity (η), hardness, cohesiveness and springiness. Additionally, a numerical optimization was performed. Two ice creams containing milk cream and oleogel, respectively were compared under the optimization conditions. Results suggest oleogels obtained from the microfluidization of whey and high oleic palm oil are viable for the replacement of cream in the production of ice cream.

2013 ◽  
Author(s):  
O. A. Adetola ◽  
J. O. Olajide ◽  
A. P. Olalusi

Author(s):  
M. Serdar Akin ◽  
Busra Goncu ◽  
Mutlu B. Akin

In this study, the possibility of replacing stabilizers with microbial transglutaminase (MTG) enzyme in fat-reduced ice cream production was studied. In addition, the stage of adding (before or after the heat treatment) the MTG enzyme to ice cream was also investigated. Five different ice creams (A and C containing 1 unit MTG/g protein without stabilizer, B and D containing 0.5 unit MTG/g protein and 0.35 % stabilizer, which also consist of the mixture of Carrageenan (E 407), Guar gum (E 412), Xanthan gum (E 415) and Sodium alginate (E 401), and E (control) containing 0.7 % stabilizer) were manufactured. MTG has been added to samples A and B after heat treatment while it was added to C and D samples before the heat treatment. An experimental analysis related to the overrun, viscosity melting properties, pH, titratable acidity, dry matter, fat, protein, sensorial and microstructural properties of ice creams was carried out. According to the results, the amount and the adding stage of MTG significantly affected overrun, melting, viscosity, coldness, firmness, smoothness, mouth coating, color, appearance, taste, smell scores, and also microstructure of ice creams (p<0.01). Results also showed that MTG could be used together with other stabilizers after heat treatment in the production of ice cream. Moreover, our findings demonstrated that sample B was the closest to control in terms of sensorial properties.


2020 ◽  
Vol 69 (6) ◽  
pp. 573-584
Author(s):  
Ziyang Xu ◽  
Zhan Ye ◽  
Youdong Li ◽  
Jinwei Li ◽  
Yuanfa Liu

2012 ◽  
Vol 51 (15) ◽  
pp. 5438-5442 ◽  
Author(s):  
Teck-Sin Chang ◽  
Hassan Masood ◽  
Robiah Yunus ◽  
Umer Rashid ◽  
Thomas S. Y. Choong ◽  
...  

2018 ◽  
Author(s):  
Chiara Gambi ◽  
Fiona Gorrie ◽  
Martin John Pickering ◽  
Hugh Rabagliati

Language processing in adults is facilitated by an expert ability to generate detailed predictions about upcoming words. This may seem like an acquired skill, but some models of language acquisition assume that the ability to predict is a pre-requisite for learning. This raises a question: Do children learn to predict, or do they predict to learn? We tested whether children, like adults, can generate expectations about not just the meanings of upcoming words but, also, their sounds, which would be critical for using prediction to learn about language. In two looking-while-listening experiments, we show that two-year-olds can generate expectations about meaning based on a determiner (Can you see one…ball/two…ice-creams?), but that even children as old as five do not show an adult-like ability to predict the phonology of upcoming words based on a determiner (Can you see a…ball/an…ice-cream?). Our results therefore suggest that the ability to generate detailed predictions is a late-acquired skill. We argue that prediction may not be the key mechanism driving children’s learning, but that the ability to generate accurate semantic predictions may nevertheless have facilitative effects of language development.


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