RT Journal Article
T1 Preprocessing of Spectroscopic Data Using Affine Transformations to Improve Pattern-Recognition Analysis: An Application to Prehistoric Lithic Tools
A1 Esquivel, Francisco Javier
A1 Antonio Esquivel, Jose
A1 Morgado, Antonio
A1 Romero-Bejar, Jose L.
A1 Garcia del Moral, Luis F.
K1 affine transformation
K1 archaeology
K1 flint (chert)
K1 multivariate statistics
K1 pattern recognition
K1 spectroscopy
K1 Reflectance spectroscopy
K1 Mu-m
K1 Identification
AB The analysis of spectral reflectance data is an important tool for obtaining relevant information about the mineral composition of objects and has been used for research in chemistry, geology, biology, archaeology, pharmacy, medicine, anthropology, and other disciplines. In archaeology, the use of spectroscopic data allows us to characterize and classify artifacts and ecofacts, to analyze patterns, and to study the exchange of materials, etc., as well as to explain some properties, such as color or post-depositional processes. The spectroscopic data are of the so-called "big data" type and must be analyzed using multivariate statistical techniques, usually principal component analysis and cluster analysis. Although there are different transformations of the raw data, in this paper, we propose preprocessing by means of an affine transformation. From a mathematical point of view, this process modifies the values of reflectance for each spectral signature scaling them into a [0, 1] interval using minimum and maximum values of reflectance, thus highlighting the features of spectral curves. This method optimizes the characteristics of amplitude and shape, reduces the influence of noise, and improves results by highlighting relevant features as peaks and valleys that may remain hidden using the raw data. This methodology has been applied to a case study of prehistoric chert (flint) artifacts retrieved in archaeological excavations in the Andevalo area located in the Archaeological Museum of Huelva (Huelva, Andalusia). The use of transformed data considerably improves the results obtained with raw data, highlighting the peaks, valleys, and the shape of spectral signatures.
PB MDPI AG
YR 2022
FD 2022-11-11
LK http://hdl.handle.net/10668/21415
UL http://hdl.handle.net/10668/21415
LA en
NO Esquivel, F.J.; Esquivel, J.A.; Morgado, A.; Romero-Béjar, J.L.; Moral, L.F.G.d. Preprocessing of Spectroscopic Data Using Affine Transformations to Improve Pattern-Recognition Analysis: An Application to Prehistoric Lithic Tools. Mathematics 2022, 10, 4250
NO This work has been funded by the Ministry of Economy and Competitiveness, Project UNGR15-CE-3531, and by the research groups AGR123 and ARCHAEOSCIENCE HUM1037 of the Junta de Andalucía.
DS RISalud
RD Apr 19, 2025