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Principal Component Analysis of Spectral Data: A Contribution to the Knowledge of the Materials Constituting Works of Art

Published online by Cambridge University Press:  26 February 2011

M. Bacci ,
S. Baronti ,
A. Casini ,
F. Lotti ,
M. Picollo  and
S. Porcinai
M. Bacci
Affiliation:
IROE, National Research Council, 50127 Firenze, Italy
S. Baronti
Affiliation:
IROE, National Research Council, 50127 Firenze, Italy
A. Casini
Affiliation:
IROE, National Research Council, 50127 Firenze, Italy
F. Lotti
Affiliation:
IROE, National Research Council, 50127 Firenze, Italy
M. Picollo
Affiliation:
IROE, National Research Council, 50127 Firenze, Italy
S. Porcinai
Affiliation:
IROE, National Research Council, 50127 Firenze, Italy
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Abstract

The use of totally non-destructive techniques such as image spectroscopy for diagnosing paintings makes it possible to obtain a large amount of spectral data that provides information concerning the composition of works of art. Here, we stress how statistical treatments, such as principal component analysis (PCA), applied to 2-D data, can contribute to a better knowledge of the work of art itself and of the distribution of the materials that constitute it.

Laboratory tests, as well as applications to actual paintings, will be presented and discussed.

Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 462: Symposium DD – Materials Issues in Art and Archaeology V , 1996 , 39
Copyright
Copyright © Materials Research Society 1997

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References

REFERENCES

1. Bacci, M., Baronti, S., Casini, A., Lotti, F., Picollo, M. and Casazza, O. in Materials Issues in Art and Archaeology HI. edited by Vandiver, P.B., Druzik, J.R., Wheeler, G.S. and Freestone, I.C. (Mater. Res. Soc. Proc. 267, Pittsburgh, PA, 1992), p. 265–283.Google Scholar
2. Bacci, M. and Picollo, M., Studies in Conservation 41, p. 136–144 (1996).Google Scholar
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