Continuous processes offer more environmentally friendlier beer production compared to the batch production. However, the continuous production of mashing has not become state-of-the-art in the brewing industry. The controllability and flexibility of this process still has hurdles for practical implementation, but which are necessary to react to changing raw materials. Once overcome, a continuous mashing can be efficiently adapted to the raw materials. Both mean residence time and temperature were investigated as key parameters to influence the extract and fermentable sugar content of the wort. The continuous mashing process was implemented as continuous stirred tank reactor (CSTR) cascade consisting of mashing in (20°C), protein rest (50°C), β-amylase rest (62-64°C), saccharification rest (72°C) and mashing out (78°C). Two different temperature settings for the β-amylase rest were investigated with particular emphasis on fermentable sugars. Analysis of Variance (ANOVA) and a post-hoc analysis showed that the mean residence time and temperature settings were suitable control parameters for the fermentable sugars. In the experimental conditions, the most pronounced effect was with the β-amylase rest. These results broaden the understanding of heterogenous CSTR mashing systems about assembly and selection of process parameters

With the implementation of EU Directive 2019/904, which mandates a minimum of 25% recycled polyethylene terephthalate (rPET) in single-use beverage bottles by 2025, there is an urgent need for reliable analytical methods to verify regulatory compliance. This study evaluates a data-driven approach to quantify and classify recycled PET based on near infrared (NIR) and mid infrared (MIR) spectral data.Investigations were conducted on two sample systems: industrial preforms containing various recyclate types and electrospun nanofiber tissues with controlled rPET concentrations (0–100%). Data analysis utilized Random Forest models for classification and Lasso regression for quantitative prediction.The results demonstrate that NIRS data allows for robust classification of recyclate types in preforms, achieving precision and recall values exceeding 0.8. For quantitative determination in the fiber samples, the multimodal data fusion of NIRS and MIRS spectra yielded the highest accuracy, with a coefficient of determination of R2 = 0.93 and a Root Mean Square Error of Prediction (RMSEP) of 8.24%. However, leave-one-out validation indicated a decrease in performance for unknown material compositions, where the average prediction error rose to 21.97%. Overall, these findings highlight the potential of spectroscopic techniques for monitoring recycled content within a data-driven framework. To ensure practical application in heterogeneous material streams, future research must focus on improving model generalization through larger, more diverse calibration datasets and advanced transfer learning to bridge the gap between controlled laboratory settings and variable industrial waste.