Unlocking potential of hyperspectral imaging: Enhancing bread quality

This study highlights a cutting-edge application of hyperspectral imaging (HSI) for single-grain sorting of wheat based on protein content. It offers a transformative approach to increase grain homogeneity by protein content and improve dough and bread quality. Leveraging advanced imaging and data-driven classification, we identified significant protein variation within a single grain batch, enabling the separation into high-protein wheat (HPW) and low-protein wheat (LPW) fractions. HPW grains resulted in superior rheological performance, including greater dough stability and reduced softening. Microstructural analysis confirmed fibrous gluten matrix in HPW dough, in contrast to fragmented structure in LPW. This structural difference determines higher-level bread quality, with HPW loaves showing finer crumb structure, higher specific volume, darker crust colour, and enhanced bread aroma, indicating intensified Maillard reaction. These findings demonstrate HSI as a powerful tool in modern cereal science, enabling precise prediction of protein content in grain while sorting wheat for tailored end-use performance.