# PIXE-quantified AXSIA : elemental mapping by multivariate spectral analysis.

## Abstract

Automated, nonbiased, multivariate statistical analysis techniques are useful for converting very large amounts of data into a smaller, more manageable number of chemical components (spectra and images) that are needed to describe the measurement. We report the first use of the multivariate spectral analysis program AXSIA (Automated eXpert Spectral Image Analysis) developed at Sandia National Laboratories to quantitatively analyze micro-PIXE data maps. AXSIA implements a multivariate curve resolution technique that reduces the spectral image data sets into a limited number of physically realizable and easily interpretable components (including both spectra and images). We show that the principal component spectra can be further analyzed using conventional PIXE programs to convert the weighting images into quantitative concentration maps. A common elemental data set has been analyzed using three different PIXE analysis codes and the results compared to the cases when each of these codes is used to separately analyze the associated AXSIA principal component spectral data. We find that these comparisons are in good quantitative agreement with each other.

- Authors:

- (Sandia National Labs, Livermore, CA)
- (University of Guelph, Guelph, ON, Canada)
- (CSIRO Exploration and Mining Bayview Road, Clayton VIC, Australia)
- (Primecore Systems, Albuquerque, NM,)

- Publication Date:

- Research Org.:
- Sandia National Laboratories

- Sponsoring Org.:
- USDOE

- OSTI Identifier:
- 969117

- Report Number(s):
- SAND2005-4116C

TRN: US201001%%263

- DOE Contract Number:
- AC04-94AL85000

- Resource Type:
- Conference

- Resource Relation:
- Conference: Proposed for presentation at the 17th International Conference on Ion Beam Analysis 2005 held June 26-July 1, 2005 in Sevilla, Spain.

- Country of Publication:
- United States

- Language:
- English

- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; MULTIVARIATE ANALYSIS; A CODES; PIXE ANALYSIS; SPECTRA; IMAGES

### Citation Formats

```
Doyle, Barney Lee, Antolak, Arlyn J., Campbell, J. L., Ryan, C. G., Provencio, Paula Polyak, Barrett, Keith E., and Kotula, Paul Gabriel.
```*PIXE-quantified AXSIA : elemental mapping by multivariate spectral analysis.*. United States: N. p., 2005.
Web.

```
Doyle, Barney Lee, Antolak, Arlyn J., Campbell, J. L., Ryan, C. G., Provencio, Paula Polyak, Barrett, Keith E., & Kotula, Paul Gabriel.
```*PIXE-quantified AXSIA : elemental mapping by multivariate spectral analysis.*. United States.

```
Doyle, Barney Lee, Antolak, Arlyn J., Campbell, J. L., Ryan, C. G., Provencio, Paula Polyak, Barrett, Keith E., and Kotula, Paul Gabriel. Fri .
"PIXE-quantified AXSIA : elemental mapping by multivariate spectral analysis.". United States.
```

```
@article{osti_969117,
```

title = {PIXE-quantified AXSIA : elemental mapping by multivariate spectral analysis.},

author = {Doyle, Barney Lee and Antolak, Arlyn J. and Campbell, J. L. and Ryan, C. G. and Provencio, Paula Polyak and Barrett, Keith E. and Kotula, Paul Gabriel},

abstractNote = {Automated, nonbiased, multivariate statistical analysis techniques are useful for converting very large amounts of data into a smaller, more manageable number of chemical components (spectra and images) that are needed to describe the measurement. We report the first use of the multivariate spectral analysis program AXSIA (Automated eXpert Spectral Image Analysis) developed at Sandia National Laboratories to quantitatively analyze micro-PIXE data maps. AXSIA implements a multivariate curve resolution technique that reduces the spectral image data sets into a limited number of physically realizable and easily interpretable components (including both spectra and images). We show that the principal component spectra can be further analyzed using conventional PIXE programs to convert the weighting images into quantitative concentration maps. A common elemental data set has been analyzed using three different PIXE analysis codes and the results compared to the cases when each of these codes is used to separately analyze the associated AXSIA principal component spectral data. We find that these comparisons are in good quantitative agreement with each other.},

doi = {},

journal = {},

number = ,

volume = ,

place = {United States},

year = {2005},

month = {7}

}