Transfer learning from inorganic materials to ivory detection
Abstract This paper describes the automatic identification of ivory using Raman spectroscopy data and deep neural network (DNN) models pre-trained on open-source data from inorganic minerals. The proposed approach uses transfer learning (TL) from foundation models trained on a larger inorganic (mine...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-98915-y |
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| author | Agil Aghasanli Plamen Angelov Dmitry Kangin Jemma Kerns Rebecca Shepherd |
| author_facet | Agil Aghasanli Plamen Angelov Dmitry Kangin Jemma Kerns Rebecca Shepherd |
| author_sort | Agil Aghasanli |
| collection | DOAJ |
| description | Abstract This paper describes the automatic identification of ivory using Raman spectroscopy data and deep neural network (DNN) models pre-trained on open-source data from inorganic minerals. The proposed approach uses transfer learning (TL) from foundation models trained on a larger inorganic (minerals) spectroscopy dataset (MLROD). The results demonstrate, for the first time, the ability to transfer machine learning (ML) models from a Raman spectroscopy dataset of geological substances to classify biological ivory samples. Current identification methods, such as DNA analysis and radiocarbon dating, are costly and destructive. Recently, it was demonstrated that the use of Raman spectroscopy, a laser-based, non-destructive technique, in combination with well-known statistical techniques, has the potential to differentiate between mammoth and elephant ivory. However, this previous study had a small sample size due to difficulties in obtaining large amounts of labeled ivory data. To date, there has been no reported work on ivory classification using DNNs, and only limited studies using Raman spectra. The work proposed in this paper suggests that ML can provide high levels of accuracy in the classification of Raman spectroscopy data from ivory samples of different elephant species (up to 99.7%). This has the potential to create a quick and inexpensive method of identifying legal and illegal types of ivory to aid in enforcement of ivory trade bans. This study also demonstrated that DNN models initially pre-trained on inorganic minerals (from the MLROD dataset) that were not finetuned on ivory data had a high accuracy rate of 92%, alleviating the need for large amounts of training data from ivory specimens. Finally, the approach proposed in this paper, provides insight into the decision making and interpretation of the results using prototype-based models. This novel work demonstrates that: (1) ML methods can provide highly accurate classification of ivory from different species of elephant using data obtained using Raman spectroscopy and providing insight into the decision making (2) TL enables re-purposing the models trained on larger mineral datasets of inorganic materials (such as MLROD) to discriminating between the classes of ivory, containing inorganic and organic biological components, for the first time transgressing between non-biological and biological samples (3) the proposed method allows both training from labelled samples of ivory and the identification of unknown ivory samples through prototype-based methods. |
| format | Article |
| id | doaj-art-1d32b70b244e46cdbb9927dc3a26b92f |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1d32b70b244e46cdbb9927dc3a26b92f2025-08-20T01:47:29ZengNature PortfolioScientific Reports2045-23222025-05-0115111310.1038/s41598-025-98915-yTransfer learning from inorganic materials to ivory detectionAgil Aghasanli0Plamen Angelov1Dmitry Kangin2Jemma Kerns3Rebecca Shepherd4School of Computing and Communications, Lancaster UniversitySchool of Computing and Communications, Lancaster UniversitySchool of Computing and Communications, Lancaster UniversityLancaster Medical School, Lancaster UniversityBristol School of Anatomy, University of BristolAbstract This paper describes the automatic identification of ivory using Raman spectroscopy data and deep neural network (DNN) models pre-trained on open-source data from inorganic minerals. The proposed approach uses transfer learning (TL) from foundation models trained on a larger inorganic (minerals) spectroscopy dataset (MLROD). The results demonstrate, for the first time, the ability to transfer machine learning (ML) models from a Raman spectroscopy dataset of geological substances to classify biological ivory samples. Current identification methods, such as DNA analysis and radiocarbon dating, are costly and destructive. Recently, it was demonstrated that the use of Raman spectroscopy, a laser-based, non-destructive technique, in combination with well-known statistical techniques, has the potential to differentiate between mammoth and elephant ivory. However, this previous study had a small sample size due to difficulties in obtaining large amounts of labeled ivory data. To date, there has been no reported work on ivory classification using DNNs, and only limited studies using Raman spectra. The work proposed in this paper suggests that ML can provide high levels of accuracy in the classification of Raman spectroscopy data from ivory samples of different elephant species (up to 99.7%). This has the potential to create a quick and inexpensive method of identifying legal and illegal types of ivory to aid in enforcement of ivory trade bans. This study also demonstrated that DNN models initially pre-trained on inorganic minerals (from the MLROD dataset) that were not finetuned on ivory data had a high accuracy rate of 92%, alleviating the need for large amounts of training data from ivory specimens. Finally, the approach proposed in this paper, provides insight into the decision making and interpretation of the results using prototype-based models. This novel work demonstrates that: (1) ML methods can provide highly accurate classification of ivory from different species of elephant using data obtained using Raman spectroscopy and providing insight into the decision making (2) TL enables re-purposing the models trained on larger mineral datasets of inorganic materials (such as MLROD) to discriminating between the classes of ivory, containing inorganic and organic biological components, for the first time transgressing between non-biological and biological samples (3) the proposed method allows both training from labelled samples of ivory and the identification of unknown ivory samples through prototype-based methods.https://doi.org/10.1038/s41598-025-98915-y |
| spellingShingle | Agil Aghasanli Plamen Angelov Dmitry Kangin Jemma Kerns Rebecca Shepherd Transfer learning from inorganic materials to ivory detection Scientific Reports |
| title | Transfer learning from inorganic materials to ivory detection |
| title_full | Transfer learning from inorganic materials to ivory detection |
| title_fullStr | Transfer learning from inorganic materials to ivory detection |
| title_full_unstemmed | Transfer learning from inorganic materials to ivory detection |
| title_short | Transfer learning from inorganic materials to ivory detection |
| title_sort | transfer learning from inorganic materials to ivory detection |
| url | https://doi.org/10.1038/s41598-025-98915-y |
| work_keys_str_mv | AT agilaghasanli transferlearningfrominorganicmaterialstoivorydetection AT plamenangelov transferlearningfrominorganicmaterialstoivorydetection AT dmitrykangin transferlearningfrominorganicmaterialstoivorydetection AT jemmakerns transferlearningfrominorganicmaterialstoivorydetection AT rebeccashepherd transferlearningfrominorganicmaterialstoivorydetection |