Related Experiment Video
Updated: Aug 5, 2026

09:23
Using Extraordinary Optical Transmission to Quantify Cardiac Biomarkers in Human Serum
Published on: December 13, 2017
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.
Talanta
|August 1, 2026
Summary
This study introduces a machine learning-powered sensor chip for authenticating Panax ginseng. It rapidly detects adulteration, ensuring product quality and safety for consumers.
Area of Science:
- Analytical Chemistry
- Biotechnology
- Materials Science
Background:
- Panax ginseng is valued for its bioactivities, but faces significant challenges with adulteration and counterfeiting.
- Ensuring the authenticity and quality of ginseng products is crucial for consumer safety and market integrity.
Purpose of the Study:
- To develop a rapid, low-cost, and instrument-free method for ginseng authentication and quality control.
- To create a machine learning-assisted superwettable colorimetric sensor array microchip for detecting ginseng adulteration.
Main Methods:
- A colorimetric sensor array microchip utilizing 3-nitrophenylboronic acid to target diol groups in ginseng monosaccharides.
- Employing pH response and indicator displacement assay (IDA) mechanisms to generate distinct colorimetric fingerprints.
- Applying machine learning algorithms, including support vector regression (SVR), for classification and quantitative analysis.
Main Results:
- The sensor microchip achieved high accuracy in ginseng species identification (96.49%) and authenticity classification (99.02%).
- Support vector regression demonstrated excellent predictive performance (R² = 0.9869) for quantifying adulteration ratios.
- The method was successfully validated on commercial ginseng samples, providing reliable results.
Conclusions:
- The proposed machine learning-assisted sensor array offers a rapid and effective solution for on-site quality control of ginseng products.
- This technology addresses critical issues of adulteration and counterfeiting in the ginseng market.
- The developed method provides a valuable tool for ensuring the authenticity and quality of Panax ginseng.
