화학공학소재연구정보센터
Journal of Food Engineering, Vol.79, No.3, 1020-1032, 2007
Influence of aeration conditions on physical and sensory properties of aerated cake batter and biscuits
We have characterised the influence of the operating conditions (rotation speed and aeration duration) during the aeration step on the properties of batter and biscuits and studied the relation between the properties of the batter (overrun, theological properties, bubble size) and those of the biscuit (crumb texture, dimensions, crumb density, moisture content, sensory properties of the biscuit, NADH and tryptophan fluorescence spectra). It was shown that batter and biscuit properties were influenced by the conditions of aeration. The major changes mostly took place in the first 20 min of aeration. The main trends for batter properties were that OR and bubble number increased with duration, whereas flow index first increased and then decreased with duration. OR and consistency index were lower at 1000 rpm. The main trends for biscuit properties were that for a 1-min duration central height of biscuits was high, and work and crumb density were low. As duration increased, central height of biscuits decreased, work slightly increased and crumb density increased. Concerning the effect of rotation speed, it was shown that work, maximum force and crumb density had higher values at 1000 rpm than at other rotation speeds. The positive linear relations between crumb density and maximum force or work confirmed that the densification of the crumb led to a stronger mechanical resistance of the biscuit. As regards the relations between the properties of the batter and the biscuits, CCA (canonical correlation analysis) results showed significant links between batter and biscuit texture and between the set of sensory indicators and batter properties. However, no significant link was found between batter and biscuit aeration. Finally, significant relations between NADH spectra and textural properties and between NADH spectra and crumb density were shown using CCA and PLSR (partial least squares regression). (c) 2006 Elsevier Ltd. All rights reserved.