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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

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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.