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Liggins, F., Vichi, A., Liu, W., Hogg, A., Kogou, S. 
ORCID: 0000-0003-1722-7626, Chen, J. and Liang, H. ORCID: 0000-0001-9496-406X,
2022.
Hyperspectral imaging solutions for the non-invasive detection and automated mapping of copper trihydroxychlorides in ancient bronze.
Heritage Science, 10 (1): 142.
Abstract
Ancient bronze is subject to complex degradation which can lead, in cases where copper chlorides are present, to a cyclic and self-sustaining degradation process commonly referred to as "bronze disease". If left untreated, bronze disease can eat away at a bronze object until it is entirely deteriorated. The presence of copper trihydroxychlorides is indicative that this process is underway and therefore the detection of these corrosion products is necessary in guiding conservation of ancient bronze artefacts. In this paper we present a high spatial/spectral resolution short wave infrared (SWIR) imaging solution for mapping copper trihydroxychlorides in ancient bronze, combining hyperspectral imaging with an in-house developed unsupervised machine learning algorithm for automated spectral clustering. For this work, verification was obtained through use of an in-house developed reference database of typical ancient bronze corrosion products from several archaeological sites, and from collections of the National Museum of China. This paper also explores the suitability, and limitations, of a visible to near-infrared (VNIR) hyperspectral imaging system as a more accessible solution for mapping copper trihydroxychlorides associated with bronze disease. We suggest that our hyperspectral imaging solution can provide a non-invasive, rapid, and high resolution material mapping within and across bronze objects, particularly beneficial for analysing large collections in a museum setting.
| Item Type: | Journal article | ||||||
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| Publication Title: | Heritage Science | ||||||
| Creators: | Liggins, F., Vichi, A., Liu, W., Hogg, A., Kogou, S., Chen, J. and Liang, H. | ||||||
| Publisher: | Springer Science and Business Media LLC | ||||||
| Date: | 2022 | ||||||
| Volume: | 10 | ||||||
| Number: | 1 | ||||||
| Identifiers: |
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| Rights: | © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. | ||||||
| Divisions: | Schools > School of Science and Technology | ||||||
| Record created by: | Linda Sullivan | ||||||
| Date Added: | 18 Oct 2022 10:03 | ||||||
| Last Modified: | 18 Oct 2022 10:33 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/47264 |
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