ArticleMikrochimica acta2026
Machine learning-assisted colorimetric serum phosphate detection based on sweet potato-derived carbon dots.
Article in Mikrochimica acta, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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9 authors.
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Abstract
A dual-robust, portable colorimetric sensing platform was developed by integrating purple sweet potato-derived carbon dots (PF-CDs) with machine learning-assisted signal processing. Serving as a highly stable, green nano-reductant, the PF-CDs effectively circumvent the autoxidation issues of conventional reagents, efficiently triggering the molybdenum blue reaction to produce a reliable macroscopic colorimetric response. To decouple these signals from environmental and matrix noise, a smartphone-based imaging system coupled with an machine learning algorithm was deployed for precise color recognition and automated quantitative determination. This integrated platform enables rapid phosphate detection within 60 min, exhibiting a broad linear range of 0.1-5.0 mM, a low limit of detection (LOD) of 0.03 mM, and an exceptional prediction accuracy of 99%. Ultimately, by synergizing chemical stability with analytical precision, this strategy offers a highly practical, low-cost, and robust paradigm for POC clinical phosphorus monitoring.
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