Prediction models to evaluate baking quality instruments for commercial wheat flour

Abstract

AbstractBackground and ObjectivesLoaf volume is the main indicator of wheat flour quality, but test baking has major limitations. Here, prediction models were used to evaluate which methodology best captured the baking quality in Swedish commercial wheat flour and if the chemical composition of flour increased prediction accuracy.FindingsFlour type (e.g., winter vs. spring wheat) affected prediction model results significantly. Thus, separate prediction models should be developed for each flour type. Combining data from alveograph, farinograph, and glutomatic tests with protein and damaged starch gave the best prediction results. The main loaf volume predictors were dough strength for winter wheat, stability for spring wheat, and extensibility for flour blends. The composition of protein and arabinoxylan influenced several quality parameters but did not improve loaf volume predictions.ConclusionsBest predictions were obtained for winter wheat. Spring wheat and flour blend models contained only one latent variable, indicating that protein content was the main determinant for loaf volume in these samples.Significance and NoveltyThis study is one of few using prediction models to evaluate instrument suitability to determine loaf volume. Instruments suitable for predicting quality were determined for commercial winter wheat flour, which is the main product of Swedish mills.