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

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.

Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review

“…In this approach, the spectra are first classified into areas with similar features and then the pigments present are hand identified. The proposed strategy has been later applied on visible and near-infrared (VNIR-RIS) data for bronze corrosion products' identification in a large museum collection [52].…”

“…Some of the studies are based on XRF data, acquired in various experimental conditions (power source, acquisition time, and beam size) [50,51,59]. The RIS data cover several wavelength domains, from the most conventional VIS domain (380-750 nm [62], 400-700 nm [64], 400-720 nm [49]), extended to NIR (383-893 nm [56,57], 377-1033 nm [63], 377-1037 nm [61], 400-950 nm [53], 400-1000 nm [52,55], 822-1719 nm [54]), to the recently emerging SWIR range (930-2500 nm [52], 1000-2500 nm [27,58]).…”

“…A few studies have measured the spectral response directly on dry pigment samples as either pigment powders or pellets of a single pigment, with small data sizes up to hundreds of spectra [27,50,52,58]. Most generally, mock-up samples are prepared to imitate the historical painting materiality (binder, pigment(s), preparation layer, and support).…”

“…In the first case, the available dataset consists of data points and their labels, and the NN algorithm learns a function which maps the input to the output by learning from available labelled examples, as observed in most cases [27,[43][44][45][46][47][48][49][53][54][55][56][57][58]60,62,63]. The second type of learning, unsupervised learning, is used to analyse and cluster unlabelled data [51,52,64]. In the case of transfer learning, the model is pre-trained with a large dataset and then fine-tuned with a separate smaller dataset [50,59,61].…”

Corrosion Behaviors of Artificial Chloride Patina for Studying Bronze Sculpture Corrosion in Marine Environments

“…Particularly, copper trihydroxychlorides, such like atacamite, paratacamite, and clinoatacamite, have a highly porous and defective surface condition, which may cause dissolution because of condensation of acidic water between microcracks and crevices in the patina [8][9][10][11][12]. In addition, particulates such as dirt and dust in the atmosphere can be deposited on the porous surface, absorbing active ions and exacerbating corrosion [6,8].…”