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

A. Lux; Julie Schlanz; Alessandra Botteon; L. Monico; P. Matousek; R. I. Dima; Claudia Conti; Pietro Strobbia

doi:10.1039/d5an01176k

January 1, 2026The Analyst

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

View Full Paper

Ask AI

Bookmark

Share

Key Points

The aim is to develop micro-SORS methods for analyzing pigment degradation non-invasively.
Developed micro-SORS techniques for measuring subsurface layers.
Employed machine learning for data analysis of degradation processes.
Targeted analysis focused on cultural heritage artifacts.
Successfully demonstrated non-invasive measurement of pigment degradation.
Identified significant degradation patterns indicative of preservation needs.
Validated the use of micro-SORS in monitoring cultural heritage.

Implication

Demonstrates micro-SORS technology to analyze pigment degradation in cultural heritage, suggesting innovative preservation methods.

Abstract

The study presents 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.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Lux et al. (2026) studied this question.

synapsesocial.com/papers/69bb9279496e729e6297fdca https://doi.org/10.1039/d5an01176k

View Full Paper

Ask AI

Bookmark

Share

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading