Mashing represents a key process during the production of beer. Nonetheless, neither an online monitoring system nor a quality control exists. In this work a measuring system was developed that represents a contribution to improve the acquisition of information from the process. A filtering hydrocyclone was used to produce a continuous stream of cleared mash in which a multisensor system measured physical properties. Additionally, reference analyses were conducted in the laboratory. The so gathered data formed the basis for a multivariate process analysis. A non-linear Partial Least Squares regression model estimated the quantities extract, free amino nitrogen and β-Glucan as a measure for amylolysis, proteolysis and cytolysis respectively. A trilinear Tucker3-model classified batches according to the malt quality. Unfold Principal Component Analysis detected batches with poor malt quality on the basis of multivariate deviations from the data of a reference batch. As the strongest deviations occurred at the very beginning of the mashing, a correction of the process would then still be possible.
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Mashing represents a key process during the production of beer. Nonetheless, neither an online monitoring system nor a quality control exists. In this work a measuring system was developed that represents a contribution to improve the acquisition of information from the process. A filtering hydrocyclone was used to produce a continuous stream of cleared mash in which a multisensor system measured physical properties. Additionally, reference analyses were conducted in the laboratory. The so gathe...
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