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Dough bread from refined wheat flour partially replaced by grape peels: Optimizing the rheological properties
Author(s) -
Mironeasa Silvia,
Mironeasa Costel
Publication year - 2019
Publication title -
journal of food process engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.507
H-Index - 45
eISSN - 1745-4530
pISSN - 0145-8876
DOI - 10.1111/jfpe.13207
Subject(s) - farinograph , rheology , wheat flour , food science , absorption of water , falling number , softening , mathematics , response surface methodology , materials science , chemistry , composite material , statistics
The valorization of grape peels by‐product to supplement wheat bread has immense potential, particularly in refined wheat flour (RWF) to increase the dietary fibers. The main aims of this study were successful modeling of dough rheological properties using predictive models, investigating the effect of factors, grape peels flour (GPF) variety, level and particle size (PS) of GPF added in RWF on responses, and finally, optimizing the formulation with respect to dough rheological behavior. The rheological properties of grape peels–wheat composite flour formulated with GPF from two variety and increasing levels of GPF (0–9%) in the presence of different PS (large, L > 500 μm; medium, 200 μm > M < 500 μm, and small, S < 200 μm) of GPF was evaluated using farinograph, alveograph, and dynamic oscillation measurements. The artificial neural network models ( R 2 > .82) developed to predict dough rheological properties highlighted an improved estimation and predictive capabilities compared with response surface methodology models ( R 2 > .77). The multiobjective optimization approach allowed anticipation of the optimal value for each response in terms of dough rheological properties as function of GPF from white variety of small PS, which replaced RWF at level of 3.81%. Practical applications In the composite flour formulation to produce effectiveness and nutritive bread, optimizing dough rheology in relation to the formulation factors represents the actual need for the bread‐making industry. Dough behavior during mixing (water absorption, dough stability, dough development time, and degree of softening), alveograph parameters (dough tenacity, extensibility, and deformation energy), and dynamic rheological properties (elastic modulus, viscous modulus, loss tangent, and complex viscosity) are considered essential to optimization of grape peels–wheat composite flour formulation. The current study revealed the efficacy of modeling and optimization between response surface and artificial neural network techniques for the grape peels–wheat flour dough rheological properties. The information given from this study would help the industry to develop new bread‐making products with the desired particle size with optimum functional and nutritional properties.