Nondestructive Speciation of Solid Mixtures by Multivariate Calibration of X-Ray Absorption Near-Edge Structure Using Artificial Neural Networks and Partial Least-Squares.
-
- KUNO Akihito
- Graduate School of Arts and Sciences, The University of Tokyo
-
- MATSUO Motoyuki
- Graduate School of Arts and Sciences, The University of Tokyo
Search this article
Abstract
Two multivariate calibration methods, artificial neural networks (ANN) and partial least-squares (PLS), have been applied to the quantitative determination of iron species in solid mixtures by X-ray absorption near-edge structure (XANES). XANES spectra were successfully resolved by both methods, and the iron compounds in solid mixtures were quantified, even though the spectra of different compounds showed serious overlap. When iron compounds that were not contained in the model mixtures were subjected to the calibration model, ANN recognized the patterns of their XANES spectra as the nearest spectra of model compounds in shape, and gave more robust results than PLS. The self-absorption effect on the calculated values from XANES measured in the fluorescence mode was examined by comparing with transmission mode; it turned out that a spectral distortion by a self-absorption effect is irrelevant to the prediction performance of these multivariate calibration methods. The present study demonstrated that ANN and PLS are applicable to the chemical speciation of elements by XANES measured in the fluorescence mode.
Journal
-
- Analytical Sciences
-
Analytical Sciences 16 (6), 597-602, 2000
The Japan Society for Analytical Chemistry
- Tweet
Keywords
Details 詳細情報について
-
- CRID
- 1390001204256951680
-
- NII Article ID
- 10004955050
-
- NII Book ID
- AA10500785
-
- COI
- 1:CAS:528:DC%2BD3cXksVOitrc%3D
-
- ISSN
- 13482246
- 09106340
-
- NDL BIB ID
- 5380259
-
- Text Lang
- en
-
- Data Source
-
- JaLC
- NDL
- Crossref
- CiNii Articles
-
- Abstract License Flag
- Disallowed