Hyperspectral imaging solutions for the non-invasive detection and automated mapping of copper trihydroxychlorides in ancient bronze

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.