BACKGROUND: In bread-making technology, α-amylase activity is routinely measured with a Falling Number device to predict wheat flour quality. The aim of this study was to determine the possibility of using Mixolab parameters to assess the Falling Number (FN) index. The effects of different doses of fungal α-amylase addition on the Mixolab characteristics and FN index values were investigated. RESULTS: Principal component analysis was performed in order to illustrate the relationships between the Mixolab parameters and the FN index. To highlight the linear combination between the FN index values and the Mixolab parameters used to evaluate starch pasting properties (C3, C4, C5 and point differences C34 and C54), a multivariate prediction model was developed. Greatest precision (R = 0.728) was obtained for the linear regression FN = f(C4, C54) model. This model was tested on a different sample set than the one on which it was built. A high correlation was obtained between predictive model and measured FN index values (r = 0.896, P = 0.01). CONCLUSION: The model provides a framework to predict the evolution of the FN index, which is predicted by the torque for cooking stability (C4) and the difference between points C5 and C4 (C54). The obtained results suggested that the Mixolab device could be a reliable instrument for evaluation of the FN index values.
BACKGROUND: In bread-making technology, α-amylase activity is routinely measured with a Falling Number device to predict wheat flour quality. The aim of this study was to determine the possibility of using Mixolab parameters to assess the Falling Number (FN) index. The effects of different doses of fungal α-amylase addition on the Mixolab characteristics and FN index values were investigated. RESULTS: Principal component analysis was performed in order to illustrate the relationships between the Mixolab parameters and the FN index. To highlight the linear combination between the FN index values and the Mixolab parameters used to evaluate starch pasting properties (C3, C4, C5 and point differences C34 and C54), a multivariate prediction model was developed. Greatest precision (R = 0.728) was obtained for the linear regression FN = f(C4, C54) model. This model was tested on a different sample set than the one on which it was built. A high correlation was obtained between predictive model and measured FN index values (r = 0.896, P = 0.01). CONCLUSION: The model provides a framework to predict the evolution of the FN index, which is predicted by the torque for cooking stability (C4) and the difference between points C5 and C4 (C54). The obtained results suggested that the Mixolab device could be a reliable instrument for evaluation of the FN index values.
Authors: Jean-Philippe Ral; Alex Whan; Oscar Larroque; Emmett Leyne; Jeni Pritchard; Anne-Sophie Dielen; Crispin A Howitt; Matthew K Morell; Marcus Newberry Journal: Plant Biotechnol J Date: 2015-05-25 Impact factor: 9.803