{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T05:00:30Z","timestamp":1790226030706,"version":"4.1.0"},"reference-count":54,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100015999","name":"Lucian Blaga University of Sibiu","doi-asserted-by":"publisher","award":["LBUS-IRG-2024; LBUS-IRG-2023-09"],"award-info":[{"award-number":["LBUS-IRG-2024; LBUS-IRG-2023-09"]}],"id":[{"id":"10.13039\/501100015999","id-type":"DOI","asserted-by":"publisher"}]},{"award":["LBUS-IRG-2024; LBUS-IRG-2023-09"],"award-info":[{"award-number":["LBUS-IRG-2024; LBUS-IRG-2023-09"]}],"id":[{"id":"https:\/\/ror.org\/026gdz537","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Foods"],"abstract":"<jats:p>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 \u00b0P, and analysed 1147 hourly observations together with synchronised pH, temperature and dissolved-oxygen records. Foam was segmented with convolutional encoder\u2013decoder networks trained separately for each fermentation, using 223 annotated frames overall (19.4% of the 1147 acquired frames; 14\u201324 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; p &lt; 0.001) and on foam-free exposed area (F(5, 62.00) = 80.57; p &lt; 0.001), whereas an effect of initial extract level was not detected (p = 0.520 and p = 0.404, respectively). Mean window-specific foam height was highest at 43.37 \u00b1 12.80 mm in the &gt;10\u201318 h window, and pH declined monotonically from 4.69 to 3.94. Within each fermentation, the annotated frames were partitioned at frame level into a training and a validation subset only, with no held-out test subset and no pooled model; the class-wise evaluation recorded for nine of these per-batch models gave a mean intersection-over-union of 0.190 \u00b1 0.077 across the classes each model used (six in eight cases, five in one) and 0.371 \u00b1 0.226 for the foam class, so segmentation accuracy on fermentations unseen during training was not established. The 307 observations recorded beyond 72 h (26.8% of the dataset) lie outside the annotated temporal domain. The findings support the proof-of-concept feasibility of longitudinal, image-based foam characterisation under controlled laboratory conditions but do not establish absolute measurement accuracy, generalisation to fermentations unseen during training, or readiness for process-control deployment. The semantic-segmentation component was used as an image-analysis instrument for extracting longitudinal foam descriptors rather than as a newly proposed segmentation architecture. In a targeted experiment on a fixed batch-level partition, with three complete fermentations withheld for testing, replacing class-weighted cross-entropy with an asymmetric Tversky loss raised foam recall on sparse-foam frames from 0.074 to 0.318 without any change to the network architecture, whereas the tested attention-gated configuration, which adds skip connections and attention gates together, did not improve foam segmentation under weighted cross-entropy. Accordingly, the methodological contribution of the study resides in the longitudinal multi-batch acquisition design, integration of image-derived descriptors with synchronised physicochemical measurements, and batch-aware analysis of repeated fermentation observations. The present data do not establish architectural superiority, segmentation generalisation to unseen fermentations, or a technical advance in semantic-segmentation methodology.<\/jats:p>","DOI":"10.3390\/foods15193366","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T14:19:03Z","timestamp":1790086743000},"page":"3366","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Longitudinal Multi-Batch Characterisation of Beer Wort Fermentation Foam Using Image-Based Semantic Segmentation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1385-6097","authenticated-orcid":false,"given":"Anca","family":"Sipos","sequence":"first","affiliation":[{"name":"Department of Agricultural Science and Food Engineering, \u201cLucian Blaga\u201d University of Sibiu, Dr. I. Ra\u021biu Street, No. 7\u20139, 550012 Sibiu, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1638-1154","authenticated-orcid":false,"given":"Otto","family":"Ketney","sequence":"additional","affiliation":[{"name":"Department of Agricultural Science and Food Engineering, \u201cLucian Blaga\u201d University of Sibiu, Dr. I. Ra\u021biu Street, No. 7\u20139, 550012 Sibiu, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0840-6972","authenticated-orcid":false,"given":"Mariana-Liliana","family":"P\u0103cal\u0103","sequence":"additional","affiliation":[{"name":"Department of Agricultural Science and Food Engineering, \u201cLucian Blaga\u201d University of Sibiu, Dr. I. Ra\u021biu Street, No. 7\u20139, 550012 Sibiu, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9754-5012","authenticated-orcid":false,"given":"Romulus","family":"Iagarau","sequence":"additional","affiliation":[{"name":"Department of Agricultural Science and Food Engineering, \u201cLucian Blaga\u201d University of Sibiu, Dr. I. Ra\u021biu Street, No. 7\u20139, 550012 Sibiu, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1177\/24726303211008861","article-title":"A Machine Vision Approach for Bioreactor Foam Sensing","volume":"26","author":"Austerjost","year":"2021","journal-title":"SLAS Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Malashin, I., Martysyuk, D., Tynchenko, V., Gantimurov, A., Semikolenov, A., Nelyub, V., and Borodulin, A. 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