|BAASANDORJ, TSOGTBAYAR - North Dakota State University|
|SIMSEK, SENAY - North Dakota State University|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 6/15/2015
Publication Date: 10/1/2015
Citation: Baasandorj, T., Dykes, L., Ohm, J.-B., Simsek, S. 2015. Modeling of cumulative ash curve in hard red spring wheat [abstract]. Annual Meeting of the American Association of Cereal Chemists, October 18-21, 2015, Minneapolis, MN. Available: http://www.aaccnet.org/meetings/Documents/2015Abstracts/aacci2015abs50.htm
Technical Abstract: Analysis of cumulative ash curves (CAC) is very important for evaluation of milling quality of wheat and blending different millstreams for specific applications. The aim of this research was to improve analysis of CAC. Five hard red spring wheat genotype composites from two regions were milled on a MIAG Multomat laboratory mill. A quadratic regression was identified as a useful method to develop models of CAC obtained from the MIAG millstream data. The fitness of the quadratic regression model was very high and range of coefficients of determination was 0.988-0.999. Individual slopes of tangent lines that represented the rate of change of cumulative ash at a given point were calculated as the values of a derivative at 50% and 70% flour extraction points using the model coefficients. Flour yields of first, second, third breaks, and break dust streams were negatively correlated with the slope at 50% flour extraction. This indicated that the higher flour yield in these streams resulted in the flatter cumulative ash curve at 50% extraction. In addition, first break stream ash content showed a high and positive correlation (r=0.91, P<0.01) with the slope at 70% extraction indicating that ash content of the first break flour influenced significantly the rate of increase of cumulative ash at 70% flour extraction. In this research, we identified a quadratic regression to model CAC and derived novel parameters, slopes of tangent lines, from the models. Results indicated that information obtained from the numerical approach could improve objective analysis of CAC and consequently, enhance evaluation of milling quality of wheat samples.