The main target of industrial mills is to supply flour of stable quality. Mills therefore perform rigorous quality control, but may still overlook parameters with significant effects on baking quality. This thesis explored: (1) how flour components affect baking quality; (2) the best instruments for use in quality control; and (3) whether test baking can be reduced by implementing prediction models. During 2018-2019, two mills in southern and central Sweden collected 197 wheat flour samples, including winter wheat, spring wheat and blends thereof. Test baking and routine quality control data were supplied by the mills, with inclusion of additional quality control instruments. Key flour components were also analysed: water-extractable and -unextractable arabinoxylan, protein composition by size exclusion HPLC, and damaged starch content. Principal component analysis (PCA) was used to rapidly assess relationships between parameters and samples. Partial least square regression (PLS) was used to predict loaf volume and evaluate parameter contributions. The protein content and size distribution of polymeric protein were found to be linked to loaf volume, while protein composition in dough was further linked to dough performance. Protein content did not influence water absorption, which was mainly determined by damaged starch content and total arabinoxylan content. Among the quality control instruments evaluated, NIT and SDmatic were most efficient in measuring protein content and damaged starch, both linked to loaf volume. Alveograph parameters were strongly linked to different flour components measured, but the interpretability of parameter extensibility and tenacity was limited by the fixed amount of water used in the standard protocol. Farinograph and Glutomatic data contributed to loaf volume predictions, but both instruments displayed notable measurement errors. Mixolab proved unsuitable for quality control, due to its inability to differentiate flours when all were of acceptable quality. Winter wheat loaf volume was accurately predicted based on selected instrument parameters
