In-process investigation of the dynamics in drying behavior and quality development of hops using visual and environmental sensors combined with chemometrics

Abstract

Hops are a key ingredient for beer brewing due to their role in preservation, the creation of foam characteristics, the bitterness and aroma of the beers. Drying significantly impacts on the composition of hops which directly affects the brewing quality of beers. Therefore, it is pivotal to understand the changes during the drying process to optimize the process with the central aim of improving product quality and process performance. Hops of the variety Mandarina Bavaria were dried at 65 °C and 70 °C with an air velocity of 0.35 m/s. Bulk weights investigated were 12, 20 and 40 kg/m2 respectively. Drying times were 105, 135, and 195 and 215 min, respectively. Drying characteristics showed a unique development, very likely due to the distinct physiology of hop cones (spindle, bracteole, bract, lupilin glands). Color changes depended strongly on the bulk weight and resulting bulk thickness (ΔE 9.5 (12 kg), 13 (20 kg), 18 (40 kg)) whilst α and ß acid contents were not affected by the drying conditions (full retention in all cases). The research demonstrated that specific air mass flow is critical for the quality of the final product, as well as the processing time required. Three types of visual sensors were integrated into the system, namely Vis-VNIR hyperspectral and RGB camera, as well as a pyrometer, to facilitate continuous in-process measurement. This enabled the dynamic characterization of the drying behavior of hops. Chemometric investigations into the prediction of moisture and chromatic information, as well as selected chemical components with full and a reduced wavelength set, were conducted. Moisture content prediction was shown to be feasible (r2 = 0.94, RMSE = 0.2) for the test set using 8 wavelengths. CIELAB a* prediction was also seen to be feasible (r2 = 0.75, RMSE = 3.75), alongside CIELAB b* prediction (r2 = 0.52 and RMSE = 2.66). Future work will involve possible ways to improve the current predictive models.