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Machine learning-assisted colorimetric sensor array on a superwettable microchip for on-site authentication of
Huan Jiang1, Bingxin Ma1, Xuyang Wang1
1Beijing Key Laboratory for Bioengineering and Sensing Technology, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, 100083, P.R. China.
Abstract:
Panax ginseng is widely used for its significant bioactivities, but adulteration and quality counterfeiting remain critical issues. Herein, a machine learning-assisted superwettable colorimetric sensor array microchip is proposed for the rapid ginseng authentication. The microchip targets diol groups of ginseng-derived monosaccharides using 3-nitrophenylboronic acid as the cross-reactive receptor, generating distinct colorimetric fingerprints via pH response and indicator displacement assay (IDA) mechanisms. The excellent determination capability of the microchip was demonstrated through the classification and semi-quantitative analysis of monosaccharides in ginseng hydrolysates. Coupled with advanced machine learning algorithms, high-accuracy analysis was achieved for ginseng species identification and authenticity classification, reaching 96.49% and 99.02%, respectively. Furthermore, support vector regression (SVR) enabled precise quantitative analysis of adulteration ratios with excellent predictive performance (R = 0.9869). The proposed method was successfully applied to identify commercial ginseng samples with reliable results. This work provides a low-cost, rapid, and instrument-free strategy for on-site quality control of ginseng products.
