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使用光谱分析和微观表征技术的帆布绘画分析

Canvas Painting Analysis Using Spectroscopic Analysis and Microcharacterisation Techniques.

作者信息

Borg Braeden, Dunn Michelle, Ang Andrew S M, Villis Carl

机构信息

School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3123, Australia.

School of Engineering, Swinburne University of Technology, Hawthorn, VIC 3123, Australia.

出版信息

Sensors (Basel). 2022 Feb 13;22(4):1442. doi: 10.3390/s22041442.

Abstract

Raman spectroscopy is a well-recognised tool for the analysis of materials in canvas paintings. However, it can be difficult to interpret the peaks of the spectra without the additional context of the artwork such as the age, provenance, or colour. Reflectance spectrophotometry can be used to capture the colour of pigments, dyes, and lacquers, but is seldom used to complement Raman data. Additionally, reflectance spectrophotometry results can be influenced by the surface profile of the painting. To overcome these limitations, this work brings together three different analysis modalities to provide a singular, analytical map of the artwork. Raman spectroscopy was used to conduct the chemical identification of pigments, binding media, and varnish present in a synthetic painting sample. Reflectance spectrophotometry was applied to obtain colour information of the surface paint of the sample. Three-dimensional optical profilometry data was used to characterise the micro topology of the paint surface. These three data sets were spatially matched allowing the recorded spectroscopic data to be displayed with the corresponding colour and surface topography across the paint surface.

摘要

拉曼光谱法是一种公认的用于分析帆布油画中材料的工具。然而,如果没有诸如年代、出处或颜色等艺术品的额外背景信息,很难解释光谱的峰值。反射分光光度法可用于获取颜料、染料和清漆的颜色,但很少用于补充拉曼数据。此外,反射分光光度法的结果可能会受到绘画表面轮廓的影响。为了克服这些限制,这项工作将三种不同的分析方式结合在一起,以提供艺术品的单一分析图谱。拉曼光谱法用于对合成绘画样本中存在的颜料、粘结介质和清漆进行化学鉴定。应用反射分光光度法获取样本表面涂料的颜色信息。三维光学轮廓测量数据用于表征涂料表面的微观拓扑结构。这三个数据集在空间上进行了匹配,使得记录的光谱数据能够与整个涂料表面相应的颜色和表面形貌一起显示。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08c9/8878647/a460778fd84e/sensors-22-01442-g001.jpg

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