ArticleSensors (Basel, Switzerland)2025
DMC-LIBSAS: A Laser-Induced Breakdown Spectroscopy Analysis System with Double-Multi Convolutional Neural Network for Accurate Traceability of Chinese Medicinal Materials.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- High-Accuracy and Efficient Classification of Uranium Slag by Origin and Category via LIBS Integrated with Hybrid Machine Learning.Sensors (Basel, Switzerland) · 2026Article
- Cross-Instrument Data Utilization Based on Laser-Induced Breakdown Spectroscopy (LIBS) for the Identification ofBioengineering (Basel, Switzerland) · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
Against the background of globalization, the circulation range of traditional Chinese medicinal materials is constantly expanding, and the phenomena of mixed origins and counterfeiting are becoming increasingly serious. Tracing the origin of traditional Chinese medicinal materials is of great significance for ensuring their quality, safety, and effectiveness. Laser-induced breakdown spectroscopy (LIBS), as a rapid and non-destructive element analysis technique, can be used for the origin tracing of traditional Chinese medicinal materials. Deep learning can not only handle non-linear relationships but also automatically extract features from high-dimensional data. In this paper, LIBS is combined with deep learning, and a Double-Multi Convolutional Neural Network LIBS Analysis System (DMC-LIBSAS) is proposed for the origin tracing of the traditional Chinese medicinal material
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