About one hundred Etruscan ceramic shards dating from the VIII to the IV century BC and coming from the archaeological excavation at Pian di Civita in Tarquinia (central Italy) have been analyzed by inductively coupled plasma optical emission spectrometry and flame atomic emission spectrometry in order to settle their provenance and to acquire knowledge about the ceramic production technology. The examined shards belong to the class of the depurata pottery, a fine ware produced in Tarquinia over a long period, and are representative of different sub-classes. The samples have been analyzed for fifteen elements (Ca, Al, Mg, Fe, Ti, Cr, Cu, Ni, Zn, Mn, Zr, Sr, Na, K and Rb). The data acquired have been treated by multivariate analysis techniques such as principal component analysis and Kohonen artificial neural networks. Most of the analyzed shards have been locally produced as belonging to a unique large group. A continuity in usage of both choice of materials and technology has been recognized.
Fermo, P., Cariati, F., Ballabio, D., Consonni, V., Bagnasco Gianni, B. (2004). Classification of ancient Etruscan ceramics using statistical multivariate analysis of data. APPLIED PHYSICS. A, MATERIALS SCIENCE & PROCESSING, 79(2), 299-307 [10.1007/s00339-004-2520-6].
Classification of ancient Etruscan ceramics using statistical multivariate analysis of data
BALLABIO, DAVIDE;CONSONNI, VIVIANA;
2004
Abstract
About one hundred Etruscan ceramic shards dating from the VIII to the IV century BC and coming from the archaeological excavation at Pian di Civita in Tarquinia (central Italy) have been analyzed by inductively coupled plasma optical emission spectrometry and flame atomic emission spectrometry in order to settle their provenance and to acquire knowledge about the ceramic production technology. The examined shards belong to the class of the depurata pottery, a fine ware produced in Tarquinia over a long period, and are representative of different sub-classes. The samples have been analyzed for fifteen elements (Ca, Al, Mg, Fe, Ti, Cr, Cu, Ni, Zn, Mn, Zr, Sr, Na, K and Rb). The data acquired have been treated by multivariate analysis techniques such as principal component analysis and Kohonen artificial neural networks. Most of the analyzed shards have been locally produced as belonging to a unique large group. A continuity in usage of both choice of materials and technology has been recognized.
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