Glass transition prediction strategies based on the Couchman-Karasz equation in model confectionary systems

Glass transition prediction strategies based on the Couchman-Karasz equation in model... Our objectives were to compare the Couchman-Karasz-predicted glass transition temperature, TCK, to the measured glass transition temperature of a mixture, Tgm, of model confectionary systems and develop an empirical correction to improve the accuracy of prediction. Results showed that the original Couchman-Karasz equation fit the data better than the modified equation; although both generally overestimated Tgm. Blends containing sorbitol had the largest ΔTgmCK, where ΔTgmCK=TCK−Tgm. While Tgm varied with composition, the increase in Tgm with decreasing moisture content was linear (R2¯=0.984) and consistent across blends (4.5 ± 0.9°C/1% moisture, wb). The increase in Tgm with increasing cook temperature was best described by a polynomial model (R2¯=0.998), but adequately described by a more generalizable linear model (R2¯=0.979). Application of an empirical correction based on moisture content or cook temperature and TCK of dry ingredients reduced the average ΔTgmCK from 20.1 °C and 11.3 °C for modified and original equations, respectively, to <5.6 °C. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Food Engineering Elsevier

Glass transition prediction strategies based on the Couchman-Karasz equation in model confectionary systems

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Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier Ltd
ISSN
0260-8774
D.O.I.
10.1016/j.jfoodeng.2017.07.007
Publisher site
See Article on Publisher Site

Abstract

Our objectives were to compare the Couchman-Karasz-predicted glass transition temperature, TCK, to the measured glass transition temperature of a mixture, Tgm, of model confectionary systems and develop an empirical correction to improve the accuracy of prediction. Results showed that the original Couchman-Karasz equation fit the data better than the modified equation; although both generally overestimated Tgm. Blends containing sorbitol had the largest ΔTgmCK, where ΔTgmCK=TCK−Tgm. While Tgm varied with composition, the increase in Tgm with decreasing moisture content was linear (R2¯=0.984) and consistent across blends (4.5 ± 0.9°C/1% moisture, wb). The increase in Tgm with increasing cook temperature was best described by a polynomial model (R2¯=0.998), but adequately described by a more generalizable linear model (R2¯=0.979). Application of an empirical correction based on moisture content or cook temperature and TCK of dry ingredients reduced the average ΔTgmCK from 20.1 °C and 11.3 °C for modified and original equations, respectively, to <5.6 °C.

Journal

Journal of Food EngineeringElsevier

Published: Dec 1, 2017

References

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