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Transfer learning from inorganic materials to ivory detection.

Agil Aghasanli1, Plamen Angelov2, Dmitry Kangin2

  • 1School of Computing and Communications, Lancaster University, Bailrigg, Lancaster, Lancashire, LA1 4YW, UK. a.aghasanli1@lancaster.ac.uk.

Scientific Reports
|May 3, 2025
PubMed
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This study uses Raman spectroscopy and deep neural networks to automatically identify ivory, achieving high accuracy. Transfer learning from mineral data enables effective classification of biological ivory, offering a quick, non-destructive alternative to current methods.

Area of Science:

  • Analytical Chemistry
  • Materials Science
  • Computational Biology

Background:

  • Current ivory identification methods (DNA, radiocarbon dating) are costly and destructive.
  • Raman spectroscopy offers a non-destructive alternative for differentiating ivory types.
  • Previous studies faced limitations due to small sample sizes and lack of advanced machine learning applications.

Purpose of the Study:

  • To develop an automated method for ivory identification using Raman spectroscopy and deep neural networks (DNNs).
  • To explore the application of transfer learning (TL) from inorganic mineral datasets to biological ivory classification.
  • To establish a rapid, cost-effective, and non-destructive technique for ivory analysis.

Main Methods:

  • Utilized deep neural network (DNN) models pre-trained on inorganic mineral spectroscopy data (MLROD dataset).

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  • Applied transfer learning (TL) to adapt models for classifying Raman spectroscopy data from ivory samples.
  • Employed prototype-based models for decision-making insights and identification of unknown samples.

    • Main Results:

    • Achieved high accuracy (up to 99.7%) in classifying ivory from different elephant species.
    • Demonstrated significant accuracy (92%) using models pre-trained on inorganic minerals without ivory-specific fine-tuning.
    • Successfully transferred machine learning models from geological to biological samples for the first time.

    Conclusions:

    • Machine learning and Raman spectroscopy provide a highly accurate and efficient method for ivory identification.
    • Transfer learning effectively repurposes models from inorganic materials for biological sample analysis, reducing data requirements.
    • The proposed approach offers a novel, non-destructive tool for combating illegal ivory trade and aiding enforcement.