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This study uses non-destructive spectroscopic analysis and machine learning to identify and quantify plasticizers in historic PVC objects. This provides valuable data for managing museum collections.

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Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Computational Science

Background:

  • Polyvinyl chloride (PVC) is widely used in heritage objects.
  • Plasticizers in PVC can degrade over time, affecting object stability.
  • Accurate identification and quantification of plasticizers are crucial for conservation.

Purpose of the Study:

  • To develop and validate non-destructive methods for analyzing plasticizers in heritage PVC.
  • To create robust machine learning models for classification and quantification of plasticizers.
  • To assess the feasibility of analyzing diverse, degraded PVC collections.

Main Methods:

  • Non-destructive spectroscopic analysis (e.g., FTIR, Raman) of over 100 diverse PVC objects.
  • Chromatographic techniques for dataset creation and model validation.
  • Development and comparison of six machine learning classification algorithms.
  • Construction of regression models for plasticizer quantification.

Main Results:

  • A highly accurate classification model was developed to identify common plasticizers (DEHP, DEHT, DINP, DIDP, DINP/DIDP mixtures) and unplasticized PVC.
  • Regression models successfully quantified di(2-ethylhexyl) phthalate (DEHP) and di(2-ethylhexyl) terephthalate (DEHT).
  • The models demonstrated reliability across a large, varied collection of degraded PVC objects.

Conclusions:

  • Non-destructive spectroscopic analysis combined with machine learning enables accurate plasticizer identification and quantification in heritage PVC.
  • The developed models are robust and reliable for analyzing diverse museum collections, even with degraded materials.
  • This approach provides essential data for informed collection management and conservation strategies.