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<p>Excessive moisture in iron ore hinders pulverization, beneficiation, and smelting. Traditional detection methods struggle with complex ore compositions. This study developed a hyperspectral model to estimate moisture in Hebei magnetite. Samples were preprocessed using S-G smoothing, MSC, SNV, derivatives, and continuum removal. CARS selected optimal bands, while PSO-LSSVR improved modeling. Reflectance showed a negative correlation with moisture, with Fe³⁺ (990&#xa0;nm) and -OH (1440/1920 nm) absorption features. The models achieved R² values of 0.798 and 0.648. The integrated approach outperformed conventional methods, enabling accurate moisture detection for industrial use.</p>

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Hyperspectral analysis and inversion model study of water content in magnetite

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月16日

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  • Yang Bai,
  • Xiaoxiao Xie,
  • Jiuling Zhang,
  • Yuna Jia,
  • Handi Wang

摘要

Excessive moisture in iron ore hinders pulverization, beneficiation, and smelting. Traditional detection methods struggle with complex ore compositions. This study developed a hyperspectral model to estimate moisture in Hebei magnetite. Samples were preprocessed using S-G smoothing, MSC, SNV, derivatives, and continuum removal. CARS selected optimal bands, while PSO-LSSVR improved modeling. Reflectance showed a negative correlation with moisture, with Fe³⁺ (990 nm) and -OH (1440/1920 nm) absorption features. The models achieved R² values of 0.798 and 0.648. The integrated approach outperformed conventional methods, enabling accurate moisture detection for industrial use.