Abstract

Vegetable tanned leather presents a unique challenge to conservators and curators of heritage collections, as little is known about how its physical and chemical properties change upon deterioration. Developing a better understanding of deterioration processes would be incredibly valuable in informing the conservation, storage, and restoration of leather objects. Fourier Transform infrared spectroscopy (FTIR) used with attenuated total reflectance (ATR) is increasingly applied in the heritage sector due to its relative ease of application and potential to be non-destructive. However, whilst FTIR has been applied successfully to the understanding of deterioration in other protein-based materials such as parchment, its application to the analysis of leather has been limited, largely due to the highly complex spectra obtained. Here, we have developed multivariate statistical methods for the analysis of FTIR data obtained from a time-series of leather samples artificially degraded at different pH values. Principal component analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA) and k-means clustering, when used together, are demonstrated as powerful tools in identifying early subtle differences in the FTIR spectra as leather degrades, identifying differences occurring over time and between different environmental conditions. We show that k-means clustering of time series data was able to highlight some areas of the spectrum that might be indicative of degradation, which more common chemometric techniques could not. The methods we describe here have the potential to widen the application of FTIR as a fast, non-destructive and reliable tool for assessing the condition of archaeological and historical leather objects, ultimately leading to better informed conservation, storage and restoration of these objects.

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