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Grouping of complex substances using analytical chemistry data: A framework for quantitative evaluation and

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Advanced data analysis frameworks can group complex chemical substances like petroleum products. This helps in environmental health regulations by assessing substance similarity when exact composition is unknown.

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Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Environmental Science

Background:

  • Detailed chemical characterization of complex substances (e.g., petroleum products, environmental mixtures) is crucial for exposure assessment and manufacturing.
  • The inherent complexity and variability of these substances hinder precise analytical characterization.
  • Evaluating similarity between complex substances offers a practical approach for regulatory decision-making.

Purpose of the Study:

  • To propose and validate a framework using unsupervised and supervised analyses for optimally grouping complex substances based on analytical features.
  • To assess the impact of different data analysis and visualization methods on the characterization and grouping of complex substances.
  • To evaluate the effectiveness of various analytical techniques in characterizing complex mixtures.

Main Methods:

  • Utilized hierarchical clustering with Pearson correlation for unsupervised grouping of complex oil-derived substances.
  • Employed the Random Forest algorithm for supervised classification model development.
  • Tested the framework on two datasets: GC-MS analysis of 20 crude oil/refining products and GC-MS, GC×GC-FID, IM-MS analysis of 15 gas oil samples.
  • Quantitatively assessed clustering and classification performance using Fowlkes-Mallows index and model accuracies, respectively.

Main Results:

  • Demonstrated the framework's ability to optimally group complex substances based on analytical features.
  • Evaluated the influence of grouping methodologies, dataset size, and dimensionality reduction on grouping quality.
  • Showcased the impact of different analytical techniques (GC-MS, GC×GC-FID, IM-MS) on substance characterization.
  • Confirmed that data analysis and visualization choices significantly affect the communication of similarity for complex substances.

Conclusions:

  • The proposed framework provides a robust approach for grouping complex substances when detailed characterization is challenging.
  • Effective data analysis and visualization are key to managing the complexity and variability of chemical mixtures for regulatory purposes.
  • This methodology aids in decision-making for environmental health regulations by providing a means to assess substance similarity.