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Quantitative analysis of 16–17th century archaeological glass vessels using PLS regression of EPXMA and µ‐XRF data
Author(s) -
Lemberge Pascal,
De Raedt Ine,
Janssens Koen H.,
Wei Fuzhong,
Van Espen Pierre J.
Publication year - 2000
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/1099-128x(200009/12)14:5/6<751::aid-cem622>3.0.co;2-d
Subject(s) - regression analysis , archaeology , regression , mathematics , statistics , history
In this paper we report on the application of partial least squares regression (PLS) to electron probe X‐ray microanalysis (EPXMA) and micro X‐ray fluorescence (µ‐XRF) analysis of 16–17th century archaeological glass samples for the determination of Na 2 O, SiO 2 , K 2 O, CaO, MnO and Fe 2 O 3 . Traditional quantitative processing of the spectral data acquired by these analytical techniques is time‐consuming and requires a skilled operator. The application of PLS improves both the speed and simplicity of the analysis. A total of 180 glass samples were analyzed by EPXMA and a subset of 53 samples by µ‐XRF. These data sets serve as a basis to investigate the performance of PLS when applied to these kinds of analytical techniques. In the case of EPXMA a small training set of 25 carefully selected samples is used to build the PLS model, while for µ‐XRF all 53 samples are used. Results show that the PLS method is able to quantify the major constituents of glass (SiO 2 , K 2 O and CaO) with a relative error smaller than 3% in the case of EPXMA data and 5% in the case of µ‐XRF. The EPXMA and µ‐XRF data sets are also used to investigate how the matrix effect present in EPXMA and µ‐XRF analysis affects the predictive ability of PLS. The effect of different preprocessing techniques is studied as well. Copyright © 2000 John Wiley & Sons, Ltd.