Longitudinal Multi-Batch Characterisation of Beer Wort Fermentation Foam Using Image-Based Semantic Segmentation.

Image-derived descriptors of fermentation foam may complement conventional brewing-process measurements, but they require evaluation at the level of independent fermentation batches. We derived image-based estimates of foam height and foam-free exposed area from hourly images of 12 beer wort fermentations conducted at initial extract levels of 12, 15 and 18 °P, and analysed 1147 hourly observations together with synchronised pH, temperature and dissolved-oxygen records. Foam was segmented with convolutional encoder-decoder networks trained separately for each fermentation, using 223 annotated frames overall (19.4% of the 1147 acquired frames; 14-24 frames per batch), all acquired within the first 72 h. Because hourly frames were nested within fermentation batches, observation-level correlations were treated as exploratory. In a batch-aware sensitivity analysis based on 70 batch-by-window means, linear mixed-effects models with batch as a random intercept identified an effect of operational time window on foam height (F(5, 53.10) = 72.17;