We present a method for estimating the thickness of thin turbid layers using defocusing micro-spatially offset Raman spectroscopy (micro-SORS). The approach, applicable to highly turbid systems, enables one to predict depths in excess of those accessible with conventional Raman microscopy. The technique can be used, for example, to establish the paint layer thickness on cultural heritage objects, such as panel canvases, mural paintings, painted statues and decorated objects. Other applications include analysis in polymer, biological and biomedical disciplines, catalytic and forensics sciences where highly turbid overlayers are often present and where invasive probing may not be possible or is undesirable. The method comprises two stages: (i) a calibration step for training the method on a well characterized sample set with a known thickness, and (ii) a prediction step where the prediction of layer thickness is carried out non-invasively on samples of unknown thickness of the same chemical and physical make up as the calibration set. An illustrative example of a practical deployment of this method is the analysis of larger areas of paintings. In this case, first, a calibration would be performed on a fragment of painting of a 2016 The Author(s) Published by the Royal Society. All rights reserved. known thickness (e.g. derived from cross-sectional analysis) and subsequently the analysis of thickness across larger areas of painting could then be carried out non-invasively. The performance of the method is compared with that of the more established optical coherence tomography (OCT) technique on identical sample set. This article is part of the themed issue 'Raman spectroscopy in art and archaeology'.

Determination of thickness of thin turbid painted over-layers using micro-scale spatially offset Raman spectroscopy

Claudia Conti;Marco Realini;Chiara Colombo;Alessandra Botteon;Moira Bertasa;Jana Striova;
2016

Abstract

We present a method for estimating the thickness of thin turbid layers using defocusing micro-spatially offset Raman spectroscopy (micro-SORS). The approach, applicable to highly turbid systems, enables one to predict depths in excess of those accessible with conventional Raman microscopy. The technique can be used, for example, to establish the paint layer thickness on cultural heritage objects, such as panel canvases, mural paintings, painted statues and decorated objects. Other applications include analysis in polymer, biological and biomedical disciplines, catalytic and forensics sciences where highly turbid overlayers are often present and where invasive probing may not be possible or is undesirable. The method comprises two stages: (i) a calibration step for training the method on a well characterized sample set with a known thickness, and (ii) a prediction step where the prediction of layer thickness is carried out non-invasively on samples of unknown thickness of the same chemical and physical make up as the calibration set. An illustrative example of a practical deployment of this method is the analysis of larger areas of paintings. In this case, first, a calibration would be performed on a fragment of painting of a 2016 The Author(s) Published by the Royal Society. All rights reserved. known thickness (e.g. derived from cross-sectional analysis) and subsequently the analysis of thickness across larger areas of painting could then be carried out non-invasively. The performance of the method is compared with that of the more established optical coherence tomography (OCT) technique on identical sample set. This article is part of the themed issue 'Raman spectroscopy in art and archaeology'.
2016
Istituto per la Conservazione e la Valorizzazione dei Beni Culturali - ICVBC - Sede Sesto Fiorentino
Istituto Nazionale di Ottica - INO
Istituto di Scienze del Patrimonio Culturale - ISPC
spatially offset Raman spectroscopy
optical coherence tomography
non-invasive
thickness
cultural heritage
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/314535
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