Chemistry of Street Art: Neural Network for the Spectral Analysis of Berlin Wall Colors

Abstract

This research starts with the analysis of some fragments of the Berlin Wall street art for the characterization of the painting materials. The spectroscopic results provide a general description of the paint executive technique but more importantly open the way to a new advantage of Raman application to the analytic analysis of acrylic colors. The study highlights the correlation between peak intensity and compound percentage and explores the powerful application of deep learning for the quantification of a pigment mixture in the acrylic commercial products from Raman spectra acquired with hand-held equipment (BRAVO by Bruker). The study reveals the ability of the convolutional neural network (CNN) algorithm to analyze the spectra and predict the ratio between the coloring compounds. The reference materials for calibration and training were obtained by the dilution of commercial acrylic colors commonly practiced by street artists, using Schmincke brand paints. For the first time, Raman investigation provides valuable insights into calibrations for determining dye dilution in mixtures of commercial products, offering a new opportunity for analytical quantification with Raman hand-held spectrometers and contributing to a comprehensive understanding of artists’ techniques and materials in street art.