{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T12:00:30Z","timestamp":1772107230776,"version":"3.50.1"},"reference-count":20,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sahmyook University Research Fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>Soymilk solid content (%) is a critical quality indicator that is directly related to product classification and regulatory compliance in food manufacturing. However, conventional optical refractometer-based measurements often suffer from blurred scale boundaries and subjective reading errors, leading to poor reproducibility under varying illumination conditions. This study proposes an image-based signal analysis framework that quantitatively interprets blurred liquid-scale boundaries by analyzing pixel intensity profiles, their gradients, and effective boundary widths. Instead of relying on human visual judgment, the proposed method characterizes boundary uncertainty using Gaussian-smoothed intensity signals and derivative-based feature extraction. Quantitative validation against ground-truth concentration values over 150 images demonstrates an overall mean absolute error (MAE) of 1.90 and a root mean squared error (RMSE) of 3.85. Illumination conditions yielding stable, single-peak derivative responses achieve an overall MAE of 0.23, whereas severe illumination conditions associated with unstable or distorted derivative patterns result in substantially higher errors (MAE = 8.57, RMSE = 8.60). These results quantitatively confirm that derivative-based boundary signal stability is directly linked to measurement accuracy. By transforming visual ambiguity into quantifiable signal features, this work provides a practical and reproducible alternative to subjective refractometer readings and offers a foundation for reliability-aware optical concentration measurement systems in industrial environments.<\/jats:p>","DOI":"10.3390\/jsan15020024","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T10:33:02Z","timestamp":1772101982000},"page":"24","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Image-Based Quantification of Boundary Uncertainty for Reliable Soymilk Solid Content Measurement"],"prefix":"10.3390","volume":"15","author":[{"given":"Taeyoon","family":"Kim","sequence":"first","affiliation":[{"name":"Division of Artificial Intelligence Convergence, Sahmyook University, Seoul 01795, Republic of Korea"}]},{"given":"Minseo","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Artificial Intelligence Convergence, Sahmyook University, Seoul 01795, Republic of Korea"}]},{"given":"Sanghyun","family":"Cheong","sequence":"additional","affiliation":[{"name":"Sahmyook Foods, Cheonan-si 31033, Chungcheongnam-do, Republic of Korea"}]},{"given":"Chunghwa","family":"Song","sequence":"additional","affiliation":[{"name":"Sahmyook Foods, Cheonan-si 31033, Chungcheongnam-do, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2867-1127","authenticated-orcid":false,"given":"Han-Cheol","family":"Ryu","sequence":"additional","affiliation":[{"name":"Division of Artificial Intelligence Convergence, Sahmyook University, Seoul 01795, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"ref_1","first-page":"256","article-title":"Nutritional and Physicochemical Characterization of Soymilk","volume":"16","author":"Basharat","year":"2020","journal-title":"Int. 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