Contrary to methods available for wheat and barley, suitable indicators for oat quality are not yet developed. These indicators would be necessary in cereal industry to know what parameters and characteristics are describing the properties of raw material, and for what kind of food applications it is the most suitable. This project will increase the knowledge on what are the main indicators and levels of oat quality (chemical, physical) in different food processes, and how to ensure uniform quality of products among varying batches of raw materials.
Technologies and methods are designed and used to find relations between chemical composition, physical properties and techno-functional characteristics of different oat cultivars and oat batches. Machine learning methods will be used to screen the most relevant properties of oat raw materials. This information will help to predict optimal end-product quality for solid to moist food products.
This 2-years project is funded from Food from Finland 2.0 program by Business Finland.
The project partners are VTT (coordinator), University of Helsinki, University of Turku, Luke and companies.
The research group in University of Helsinki includes Tuula Sontag-Strohm (team leader), Vieno Piironen, Anna-Maija Lampi, Marjo Pulkkinen and Saara Sammalisto.