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Plasmonic artificial inspector for herbal medicines via surface-enhanced Raman spectroscopy and deep learning
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  • Published: 05 February 2026

Plasmonic artificial inspector for herbal medicines via surface-enhanced Raman spectroscopy and deep learning

  • Hongdoo Kim1 na1,
  • Jemin Lee1 na1,
  • Sung Won Kim2 &
  • …
  • Hyung Gyu Park1 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Chemistry
  • Optics and photonics

Abstract

For the safety monitoring of herbal medicines (HMs), HM inspectors carry out an organoleptic examination before acceptance for market supply. The organoleptic test processes are often regarded as labor-intensive, thus calling for efficient and reliable aids. Here, we propose a plasmonic artificial HM inspector based on a collaboration between surface-enhanced Raman spectroscopy (SERS) and deep learning (DL). Inherently, a SERS spectrum of an HM specimen contains several peaks that match bioactive compounds in the sample, yielding so-called fingerprint information of HM. Besides, its rapid, few-second data-acquisition speed lends the SERS-DL analysis adaptability to a complementary inspection method for organoleptic examination. Regarding the accuracy and reliability of this new method, the synergistic integration of SERS with DL attains ~95% accuracy in labor-saving differentiation of 35 HM species with similar appearances or of the same genus. Our SERS-DL-based analysis can potentially aid the organoleptic HM inspection and help upgrade the HM database, along with images and other analytical chemistry data.

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Data availability

Data available on request from the authors.

Code availability

Code availability on request from the authors.

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Funding

This work was funded by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. 2020R1A3B2079741). A part of this work was supported by the Po-Ca Networking Groups funded by the Postech-Catholic Biomedical Engineering Institute (PCBMI) (No. 2.0080459.01).

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Author notes

  1. Hongdoo Kim and Jemin Lee contributed equally to this work.

Authors and Affiliations

  1. Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, 37673, Gyeongbuk, Republic of Korea

    Hongdoo Kim, Jemin Lee & Hyung Gyu Park

  2. Department of Otolaryngology-Head and Neck Surgery, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea

    Sung Won Kim

Authors
  1. Hongdoo Kim
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  2. Jemin Lee
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  3. Sung Won Kim
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  4. Hyung Gyu Park
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Contributions

H.K. contributed conceptualization, methodology, investigation, and writing - original draft; J.L. contributed software, data curation, writing - original draft, and visualization; S.W.K. contributed conceptualization; H.G.P. contributed conceptualization, writing - review & editing, and supervision.

Corresponding author

Correspondence to Hyung Gyu Park.

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Kim, H., Lee, J., Kim, S.W. et al. Plasmonic artificial inspector for herbal medicines via surface-enhanced Raman spectroscopy and deep learning. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38497-5

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  • Received: 08 December 2025

  • Accepted: 29 January 2026

  • Published: 05 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-38497-5

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Keywords

  • Surface-enhanced Raman spectroscopy
  • Deep learning
  • Herbal medicines
  • SERS-DL analysis
  • HM classification
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