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Multivariate calibrations for the SR‐TXRF determination of trace concentrations of lead and arsenic in the presence of bromine
Author(s) -
Nagata Noemi,
PeraltaZamora Patricio G.,
Poppi Ronei J.,
Perez Carlos A.,
Bueno Maria Izabel M. S.
Publication year - 2005
Publication title -
x‐ray spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.447
H-Index - 45
eISSN - 1097-4539
pISSN - 0049-8246
DOI - 10.1002/xrs.856
Subject(s) - arsenic , bromine , chemistry , multivariate statistics , univariate , analytical chemistry (journal) , partial least squares regression , x ray fluorescence , fluorescence spectrometry , calibration , lead (geology) , detection limit , radiochemistry , fluorescence , environmental chemistry , chromatography , mathematics , physics , statistics , organic chemistry , quantum mechanics , geomorphology , geology
Abstract A synchrotron radiation source and total reflection x‐ray fluorescence (SR‐TXRF) spectrometry were used for the determination of lead and arsenic in aqueous samples. To overcome the serious spectral interference between the two species and the overlap of another interfering element (bromine), a partial least‐squares regression (PLSR) method was used. The calibration models PLSR2 and PLSR1 were based on the x‐ray fluorescence emission signals (9.550–13.663 keV) for a set of 26 different mixtures containing the elements of interest, lead and arsenic, as well as bromine. The results obtained by PLSR1 and PLSR2 were compared with those obtained by the conventional univariate methodology. In the latter case, the areas of the secondary emission lines (Lβ for lead and Kβ for arsenic) were used to elaborate the analytical curves. The capacity of all optimized models was verified using five synthetic samples (external validation). Subsequently the best model was used to determine lead and arsenic recovery capacities when these metals are retained on two ion‐exchange resins (Dowex 50‐X8 and Dowex 1‐X8). The best multivariate model (PLSR1) allowed the determination of lead and arsenic with root mean square errors of prediction (RMSEPs) of 0.03 and 0.24 mg l −1 , respectively. The reduction of this parameter, with respect to the values obtained by conventional univariate methodology (0.26–0.03 mg l −1 for lead and 0.30–0.24 mg l −1 for arsenic), indicates that the proposed multivariate methodology really overcomes the problems associated with spectral interferences and minimizes the influence of an interfering agent (bromine). Copyright © 2005 John Wiley & Sons, Ltd.