Foaming capacity describing

For the studies presented in this chapter, samples of peanut and cottonseed meal suspensions were evaluated for foam capacity, stability, and viscosity measurements as described by Cherry and coworkers (23, 24, 22). Vegetable protein suspensions at the appropriate concentration and pH were whipped in a Waring-type blender. After blending, the whipped products were transferred to a graduated cyclinder. Milliliters of foam were recorded immediately and at various time intervals to determine capacity and stability. A Brookfield viscometer and... 

Empirical multiple linear regression models were developed to describe the foam capacity and stability data of Figures 2 and 4 as a function of pH and suspension concentration (Tables III and IV). These statistical analyses and foaming procedures were modeled after data published earlier (23, 24, 29, 30, 31). The multiple values of 0.9601 and 0.9563 for foam capacity and stability, respectively, were very high, indicating that approximately 96% of the variability contributing to both of these functional properties of foam was accounted for by the seven variables used in the equation. 

Table III. Empirical multiple linear regression model describing foaming capacity as a function of pH and suspension concentration.

A method for expansion ratio determination based on measuring heat capacity of a foam is described by Fokina and Kruglyakov [30] (see also Section 8.5). 

Arcus gasification combuster is described and the principles upon which it works are explained. This combuster combines solid fuel gasification with the burning of the lean gases produced on a small capacity scale. The types of solid fuels which can be used are listed and these include segregated municipal waste and industrial waste such as rigid PU foam and plastics mixed with other materials. Uses of the gas produced are included. 

Some functional properties of quinoa flour have been described, mainly solubility, WHC, gelation, and foaming and emulsifying capacity. 

This chapter has introduced the construct of the recovery window in high-risk organizations and analyzed the 16-day window that ended in the breakup of the Columbia. Our analysis characterized the response to the foam threat as confirmatory in nature those involved discounted the risk, did not seek discordant data and perspectives, and failed to explore possible compensatory action. We have described an alternative - the exploratory response - and proposed that this pattern of behavior may provide a mechanism through which organizations can increase their collective learning capacity. 

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