Classification of small specimen uranium ores using LIBS combined with machine learning and deep learning algorithms

Jingrong Li; Xiaoliang Liu; Ming Zhang; Xiangting Meng; Hua Rong; Shaohua Sun; Zuoye Liu Zuoye Liu; Chunyan Zou; Xiaoyang Guo

doi:10.1039/d5an01326g

January 1, 2026The Analyst

Classification of small specimen uranium ores using LIBS combined with machine learning and deep learning algorithms

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Key Points

This research aims to develop classification models for small specimen uranium ores using LIBS technology combined with ML and DL algorithms.
Utilized laser-induced breakdown spectroscopy to analyze uranium ores.
Developed classification models using machine learning algorithms.
Implemented deep learning techniques for improved accuracy in classification.
Achieved high-precision classification of uranium ores.
Demonstrated rapid analysis capabilities compared to traditional methods.

Implication

Classification models based on LIBS demonstrate high precision in uranium ore identification, implying efficient analysis techniques.

Abstract

In this study, classification models based on Laser-induced breakdown spectroscopy (LIBS) technology, combined with machine learning (ML) and deep learning (DL) algorithms, were proposed to enable the rapid and high-precision...

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Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/699011032ccff479cfe57697 https://doi.org/10.1039/d5an01326g

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Classification of small specimen uranium ores using LIBS combined with machine learning and deep learning algorithms

Key Points

Implication

Abstract

Discussion

Cite This Study

Classification of small specimen uranium ores using LIBS combined with machine learning and deep learning algorithms

Key Points

Implication

Abstract

Discussion

Cite This Study

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