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<p>Natural organic matter, also named ‘humic substances’, refers mainly to dead, partly macromolecular, organic compounds occurring in waters, soils, sediments and organic waste. Natural organic matter represents a major global carbon pool influencing many processes such as climate change, food production and environmental pollution, yet its molecular structure, dynamics and fate are poorly known. Here we the compare methods for the analysis of natural organic matter, such as nuclear magnetic resonance, ultraviolet–visible spectroscopy, fluorescence spectroscopy, Fourier transform-infrared spectroscopy, size exclusion chromatography, and mass spectrometry. We detail principal component analysis, principal coordinate analysis, hierarchical cluster analysis, parallel factor analysis, two-dimensional correlation analysis, and advanced coupled matrix tensor factorization.</p>

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Statistical methods for data analysis in the characterization of natural organic matter: a review

  • 期刊论文

  • 发表日期：

    2025年11月28日

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  • Zhe Zhang,
  • Danni Cui,
  • Islam A. Abdelhafeez,
  • Katelin A. Hartzell,
  • Bangxing Ren,
  • Huan He,
  • Dionysiou Dionysios

摘要

Natural organic matter, also named ‘humic substances’, refers mainly to dead, partly macromolecular, organic compounds occurring in waters, soils, sediments and organic waste. Natural organic matter represents a major global carbon pool influencing many processes such as climate change, food production and environmental pollution, yet its molecular structure, dynamics and fate are poorly known. Here we the compare methods for the analysis of natural organic matter, such as nuclear magnetic resonance, ultraviolet–visible spectroscopy, fluorescence spectroscopy, Fourier transform-infrared spectroscopy, size exclusion chromatography, and mass spectrometry. We detail principal component analysis, principal coordinate analysis, hierarchical cluster analysis, parallel factor analysis, two-dimensional correlation analysis, and advanced coupled matrix tensor factorization.