Affiliation:
1. Technical University of Munich, Chair of Brewing and Beverage Technology Research Group Cereal Technology and Process Engineering Freising Germany
2. Department of Plant‐based Foods University of Hohenheim, Institute of Food Science and Biotechnology Stuttgart Germany
3. AB Enzymes, Application Development Baking Darmstadt Germany
Abstract
AbstractUndesired dough adhesion is still a challenge during the production of baked goods. There are various methods for determining the adhesive texture properties of dough. In the majority of scientific papers, dough stickiness is measured analytically by the force‐distance recording of dough detachment. In this study, we describe a new multi‐scale approach to compare dough adhesion phenomena in a laboratory, pilot sale and human sensory assessment. In it, the adhesive material properties of dough were investigated using a pilot scale toppling device representing dough adhesion behavior in the production process, in the laboratory by texture analysis with the Chen–Hoseney method and furthermore with a new, implemented non‐oral human sensory analysis. To simulate different dough adhesion behavior, the dough mechanical and adhesion properties were varied by applying dough‐modifying enzymes and different dough storage times. The structural changes in the different wheat dough system were compared by rheological characterization. By characterizing the different adhesion phenomena of the doughs, the sample with bacterial xylanase showed the highest values after 80 min of storage time in all three methods. Correlation analysis revealed a strong relationship between the detachment time (pilot scale) and human sensory assessment attributes (Force R = 0.81, Time R = 0.87, Distance R = 0.92, Stickiness R = 0.80) after 80 min of storage time. Even though human sensory assessment showed limits in the detectability of differences in dough adhesion behavior compared to the Chen–Hoseney method, it was better suited to predict machinability.
Subject
Pharmaceutical Science,Food Science