Spectral analysis of flour fractions and extracts to predict the baking properties of wheats from different regions and elucidation of molecular mechanisms
Prof. Dr. Katharina Scherf
- Project Group:
Lukas Buck, M.Sc.
BMWK, AiF 21711 N
University of Hohenheim, Institute of Food Science and Biotechnology, Department of Process Analytics and Cereal Science
01 March 2021
31 August 2023
An accurate prediction of the baking quality of wheat lots is very important for the en-tire value chain from wheat breeding to cultivation, trade, grain processing in the mills to the final use of the flours in bakeries. Wheat breeders can develop cultivars that are better suited to the needs of the market, farmers can adapt their cultivation condi-tions to achieving good baking quality even under reduced nitrogen fertilization and traders can market flour lots according to baking quality and prevent discrepancies between price and value. Millers have increased confidence in purchasing cereal lots. An improved prediction of the baking quality is especially important also for bakers who profit from better reproducibility of the baking quality and a lower risk of failure during production. In Germany, there are about 30 cereal breeders, hundreds of farms, that grow wheat on 3 million hectares of land, 100 cereal traders, 190 mills, that process around 7 million tons of wheat for food, and 10,000 bakeries. A large majority of businesses are SMEs that are faced with increasing price pressure and decreasing profit margins. A predictable wheat quality decreases friction along the value chain and prevents losses arising from interruptions of production or losses due to variable product quality.
The aims of the project are to generate knowledge to judge the baking quality of wheat and develop robust chemometric models to predict the baking quality based on spectroscopic measurements. The following working hypotheses will be tested: 1) the spectroscopic analysis of flour fractions and extracts yields more relevant information compared to spectra of untreated flours, 2) chemometric modelling of the spectra will enable the identification of flour fractions. The composition of these flour fractions will provide information on the link to the baking quality to select wavelengths that are suitable to build a spectroscopic prototype sensor. 3) The (bio)chemical analysis of flour fractions will generate new insights that are useful to judge the baking quality.