Defining the basis for variation in water absorption of UK wheat flours


Cereals & Oilseeds
Project code:
01 April 2016 - 30 June 2019
AHDB Cereals & Oilseeds.
AHDB sector cost:
Total project value:
Project leader:
Peter R Shewry (1), Abigail J Wood (1), Kirsty Hassall (1) Liz Howes (2), Mervin Poole (2), Paola Tosi (3) and Alison Lovegrove (1) (1) Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ (2) Heygates Ltd., Bugbrooke Mills, Northampton NN7 3QH (3) School of Agriculture, Policy and Development, University of Reading, Whiteknights Campus, Early Gate RG6 7AR


pr612-final-summary-report pr612-final-project-report

About this project


The water absorption (WA) of wheat flour is a major factor that affects bread-making performance.  The milling procedure is modified to achieve the required WA level. However, it is difficult to achieve this level with UK wheat in some years.

The study aimed to:

1. Identify factors that affect the WA of UK-grown wheat. This included a comparison of wheat grown in years with typical (2016 and 2018) and atypically low (2013 and 2017) WA levels.

2. Determine whether variation in fibre composition and properties contributed to variation in WA.

3. Determine whether variation in nitrogen fertilisation contributed to differences in WA.

The amounts and compositions of a range of components, including starch, protein and fibre components (including pentosan fractions), in white flour were determined. Cultivars/lines in the study included those used in the Defra-funded Wheat Genetic Improvement Network (WGIN) and lines developed specifically to contain different amounts of pentosan (arabinoxylan) fibre in a common genetic background.

The Farrand Equation (based on protein, starch damage and moisture content) is widely used to predict WA. This project used statistical analysis to determine whether the addition of specific traits could improve the predictions, compared to the baseline model.

Analysis of wheat datasets from 2013, 2016, 2017 and 2018 showed that the baseline model predicted 74.9% of the variance in WA. By adding Principal Components that reflected variation in grain fibre content, the prediction increased to 90.0%. The fibre fraction that gave the greatest improvement (from 74.9% to 84.2%) was β-glucan. Analysis of the lines that varied in pentosan content showed that the prediction of WA by the baseline model (86%) was increased by the addition of data for traits related to water-soluble pentosan: to 94.2% by Relative Viscosity of aqueous extracts and to 96.7% by water-extractable arabinoxylan.

Comparison of lines grown with 100kg N/Ha and 200kg N/Ha showed no effects beyond those related to grain protein content (and, therefore, already allowed for in the baseline equation).

The results show that variation in the content of fibre components, particularly soluble fibre, may account for variation in WA between cultivars and between samples grown in different environments.