Machine-Learning-Driven Multidimensional Identification of Traditional Chinese Medicine Ginkgo Folium Using SERS

doi:10.7503/cjcu20260047

Chem. J. Chinese Universities

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Machine-Learning-Driven Multidimensional Identification of Traditional Chinese Medicine Ginkgo Folium Using SERS

MA Qilin^{1, 5#}, Wu Ye^2#, Tang Danyang³, Yang Maosheng^{1, 5}, Xu Guofu¹, YANG Fan⁵, SHI Jia⁵, Yao Jianquan⁴

1. School of Electrical and Optoelectronic Engineering, West Anhui University
2. Hefei Linke Micro-Nano Technology Co., Ltd.
3. Hangzhou Leading Pharmatech Co., Ltd.
4. School of Precision Instrument and Opto-Electronics Engineering, Tianjin University 5. Tianjin Key Laboratory of Optoelectronic Detection Technology and System

Received:2026-01-26 Revised:2026-03-08 Online First:2026-03-10 Published:2026-03-10
Contact: Qi-Lin MA E-mail:qilinma@wxc.edu.cn
Supported by:
Supported by the Scientific Research Project of Universities in Anhui Province (No. 2024AH051986) and the Open Project of Tianjin Key Laboratory of Optoelectronic Detection Technology and System(No. 2025LODTS112, 2025LODTS106)

Abstract

Abstract: This study establishes a multidimensional discrimination strategy for Ginkgo Folium based on surface-enhanced Raman spectroscopy (SERS) combined with machine learning. A systematic investigation was conducted across four dimensions—geographical origin, drying process, storage quality, and pesticide residues—enabling intelligent classification and quality assessment of Ginkgo Folium. The results showed that the SERS spectra of Ginkgo Folium extracts exhibited characteristic peaks of flavonoids (quercetin, kaempferol, isorhamnetin) and ginkgolides. Based on SERS combined with partial least squares discriminant analysis (PLS-DA), 100% classification accuracy was achieved for Ginkgo Folium samples from five origins and processed by four drying methods, indicating that both origin differences and drying processes significantly affect their chemical composition. Furthermore, SERS combined with partial least squares regression (PLS) accurately predicted storage time (training set, R2 = 0.991; cross-validation set, R2 = 0.841) and enabled highly sensitive quantitative detection of pesticide residues, including glyphosate and thiram. In summary, SERS combined with machine learning provides a rapid, highly sensitive, and reliable analytical strategy for origin tracing, process monitoring, storage evaluation, and pesticide residue detection of Ginkgo Folium and other traditional Chinese medicinal materials. This approach opens a novel technical pathway for quality control and safety supervision in traditional Chinese medicines field.

Key words: Ginkgo folium, SERS, Machine learning, Multidimensional identification, Quality assessment of traditional Chinese medicine

CLC Number:

O654
O433

TrendMD:

MA Qilin, WU Ye, TANG Danyang, YANG Maosheng, XU Guofu, YANG Fan, SHI Jia, YAO Jianquan. Machine-Learning-Driven Multidimensional Identification of Traditional Chinese Medicine Ginkgo Folium Using SERS[J]. Chem. J. Chinese Universities, doi: 10.7503/cjcu20260047.

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Machine-Learning-Driven Multidimensional Identification of Traditional Chinese Medicine Ginkgo Folium Using SERS

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