{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T03:56:26Z","timestamp":1790567786107,"version":"4.1.0"},"reference-count":34,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"vor","delay-in-days":24,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ift.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Food Science"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:label\/>\n                    <jats:p>\n                      This study developed and evaluated a laboratory\u2010scale hybrid sensor\u2010vision machine learning framework for monitoring and predicting\n                      <jats:italic>mahewu<\/jats:italic>\n                      fermentation dynamics using synchronized physicochemical sensing and surface\u2010image analysis. White\u2010 and yellow\u2010maize\n                      <jats:italic>mahewu<\/jats:italic>\n                      were fermented under controlled processing conditions while pH, electrical conductivity (EC), and temperature were recorded and surface images were captured under fixed LED illumination. Images were segmented and converted into color, texture, edge, and morphology descriptors, then aligned with sensor records and raw laboratory total titratable acidity (TTA) and total soluble solids (TSS) measurements using tolerance\u2010valid matching. Semantic segmentation was evaluated separately across 96 manually annotated images from 14 fermentation\u2010run groups using nested grouped validation, producing a run\u2010balanced mean Dice coefficient of 0.8070 and mean intersection over union of 0.7195. Primary raw laboratory prediction models used 7945 matched image\u2010sensor records from 12 main fermentation runs, including 71 TTA and 87 TSS reference records. Leakage\u2010controlled models were evaluated by fermentation run using image\u2010only, sensor\u2010context, and multimodal feature sets. Raw laboratory TTA was predicted most effectively using sensor\u2010context variables with Gradient Boosting (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      = 0.8298, MAE = 61.03 , RMSE = 96.46 ). Raw laboratory TSS showed the best point\u2010estimate performance with a multimodal Random Forest model (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      = 0.5939, MAE = 0.624 , RMSE = 0.789 ). Image\u2010only models were weaker for both targets, indicating that surface descriptors are better interpreted as complementary rather than standalone information. Further validation across independent batches, lighting conditions, vessel geometries, and production environments is required before use beyond controlled laboratory conditions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Practical Applications<\/jats:title>\n                    <jats:p>\n                      <jats:italic>Mahewu<\/jats:italic>\n                      fermentation is often assessed through appearance, sourness, and producer experience. Recording pH, electrical conductivity, temperature, and surface images together provided a more objective account of within\u2010batch changes under laboratory conditions. After validation in practical production settings, this approach could help producers compare batches and make more consistent fermentation decisions.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1750-3841.71497","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T08:02:36Z","timestamp":1790323356000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Sensor\u2013Vision Machine Learning for Predicting\n                    <i>Mahewu<\/i>\n                    Fermentation Dynamics"],"prefix":"10.1111","volume":"91","author":[{"given":"Ismail","family":"Adeleke","sequence":"first","affiliation":[{"name":"Center for Cyber\u2010Physical Food, Energy and Water Systems (CCP\u2010FEWS), Department of Electrical and Electronic Engineering Science University of Johannesburg  Johannesburg South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3757-5137","authenticated-orcid":false,"given":"Oluwafemi Ayodeji","family":"Adebo","sequence":"additional","affiliation":[{"name":"Center for Innovative Food Research (CIFR), Department of Biotechnology and Food Technology University of Johannesburg  Doornfontein South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nnamdi","family":"Nwulu","sequence":"additional","affiliation":[{"name":"Center for Cyber\u2010Physical Food, Energy and Water Systems (CCP\u2010FEWS), Department of Electrical and Electronic Engineering Science University of Johannesburg  Johannesburg South 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