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Nutr."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>The illicit use of formaldehyde (FA) as a preservative in aquatic products poses grave public health risks due to its proven carcinogenicity. While gold-standard techniques like HPLC offer high precision, their implementation in real-time surveillance is hindered by labor-intensive pre-treatment and reliance on expensive instrumentation, necessitating intelligent, non-destructive sensing platforms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We present Res-Reg, a novel image-to-value deep regression framework optimized for rapid FA quantification. The sensing paradigm integrates the high specificity of the Hantzsch chromogenic reaction with digital microscopic imaging, employing a residual-based backbone and a specialized regression head for multi-layer feature aggregation. To enhance environmental robustness against matrix interference, the training dataset was augmented with Gaussian blur kernels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Systematic evaluation on 1,548 microscopic images demonstrated that Res-Reg achieved an MAE of 0.0027, MSE of 0.0028, and GM of 0.0002, significantly outperforming traditional CNN-based models by maintaining superior sensitivity in low-concentration intervals where optical signals are often obscured by turbidity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Res-Reg effectively bridges the gap between microscopic optical signatures and precise chemical concentration without complex purification. This modular and scalable technology supports the digital transformation of food quality control, providing a potent solution for proactive safety surveillance and the preservation of global nutritional standards in the Food Industry 4.0 era.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnut.2026.1859033","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:06:00Z","timestamp":1780635960000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Intelligent quantification of formaldehyde in aquatic product soaking solutions via a novel deep regression framework"],"prefix":"10.3389","volume":"13","author":[{"given":"Jing-Hao","family":"Xi","sequence":"first","affiliation":[{"name":"School of Science, Kunming University of Science and Technology, Kunming","place":["Yunnan, China"]},{"name":"Yunnan Key Laboratory of Complex Systemsand Brain-Inspired Intelligence, Kunming 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