Micro-SORS and machine learning for the non-invasive reference-free study of subsurface pigment degradation

Abstract

In this study, we present the development of micro-spatially offset Raman spectroscopy (micro-SORS) methods and data analysis routines for the study of pigment degradation processes in the cultural heritage field, exploiting micro-SORS ability to non-invasively investigate the inner portions of turbid materials. The purpose of the study is to demonstrate an automated reference-free method to visualize through micro-SORS mapping the distribution of degradation both on and below the surface. The need arises from the handling of large datasets provided by micro-SORS mapping, which are often troublesome to analyse manually and usually require prior knowledge of the sample composition. Unaged and artificially aged painted mock-up samples were analysed with micro-SORS mapping, and conventional map reconstruction was compared with both supervised and unsupervised learning methods. Representative features in the micro-SORS spectra, able to distinguish unaltered pigments and degradation products, were automatically selected through machine learning techniques, revealing hidden patterns and correlations. Through the important spectral features (wavenumbers) and clustering analysis, quantitative micro-SORS degradation maps were created to identify degradation patterns also below the sample surface. Unlike previous studies that only use supervised or unsupervised learning, both are combined in this study to ensure the relevance of the selected spectral features and discover correlations among spectra through clustering techniques. This approach can be valid also for other scientific fields, such as forensic or biomedical, where data visualization and pattern identification are essential.