Work

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

Year: 2026

Type: article

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.

Source: Scientific Reports

DOI: 10.1038/s41598-026-38497-5

Authors: Hongdoo Kim, Jemin Lee, Sung Won Kim, Hyung Gyu Park

Institutions: Pohang University of Science and Technology, Catholic University of Korea

Language: English

Topic: Gold and Silver Nanoparticles Synthesis and Applications

Subfield: Electronic, Optical and Magnetic Materials

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funders: National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea