Adaptive nonlinear mapping for feature extraction and fusion in mineral classification based on laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy

Yao Li; Mengdie Shan; Yinghao Wang; Jia Cong; Leiyi Ding; Jingjun Lin; Minchao Cui; Nan Ma

doi:10.1039/d5ja00078e

January 1, 2025Journal of Analytical Atomic Spectrometry

Adaptive nonlinear mapping for feature extraction and fusion in mineral classification based on laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy

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Key Points

To develop an adaptive nonlinear mapping method that extracts and fuses complementary spectral features from laser-induced breakdown spectroscopy and Raman spectroscopy for mineral classification.
Designed a multifunction adaptive nonlinear mapping framework for spectroscopic feature extraction.
Integrated complementary spectral datasets obtained from laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy for mineral sample analysis.
Established a multifunction nonlinear mapping architecture capable of adaptively extracting features across different spectral modalities.
Fused LIBS and Raman spectroscopic data to enhance overall mineral discrimination and classification performance.

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Implication

Experimental study demonstrates adaptive nonlinear mapping for feature fusion in mineral samples, suggesting improved classification accuracy with combined spectroscopic techniques.

Abstract

Feature extraction method for multifunction adaptive nonlinear mapping.

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Cite This Study

Li et al. (2025) studied this question.

synapsesocial.com/papers/6ab0ec9ca9a21c28a2c5a533 https://doi.org/10.1039/d5ja00078e

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Adaptive nonlinear mapping for feature extraction and fusion in mineral classification based on laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy

Key Points

No abstract yet

Implication

Abstract

Discussion

Cite This Study

Adaptive nonlinear mapping for feature extraction and fusion in mineral classification based on laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy

Key Points

No abstract yet

Implication

Abstract

Discussion

Cite This Study

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