Particle analysis using laser ablation mass spectroscopy
Abstract
The present invention provides a method of quickly identifying bioaerosols by class, even if the subject bioaerosol has not been previously encountered. The method begins by collecting laser ablation mass spectra from known particles. The spectra are correlated with the known particles, including the species of particle and the classification (e.g., bacteria). The spectra can then be used to train a neural network, for example using genetic algorithm-based training, to recognize each spectra and to recognize characteristics of the classifications. The spectra can also be used in a multivariate patch algorithm. Laser ablation mass specta from unknown particles can be presented as inputs to the trained neural net for identification as to classification. The description below first describes suitable intelligent algorithms and multivariate patch algorithms, then presents an example of the present invention including results.
- Inventors:
- Issue Date:
- Research Org.:
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1174488
- Patent Number(s):
- 6618712
- Application Number:
- 09/321,906
- Assignee:
- Sandia Corporation (Albuquerque, NM)
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- DOE Contract Number:
- AC04-94AL85000
- Resource Type:
- Patent
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
Parker, Eric P., Rosenthal, Stephen E., Trahan, Michael W., and Wagner, John S. Particle analysis using laser ablation mass spectroscopy. United States: N. p., 2003.
Web.
Parker, Eric P., Rosenthal, Stephen E., Trahan, Michael W., & Wagner, John S. Particle analysis using laser ablation mass spectroscopy. United States.
Parker, Eric P., Rosenthal, Stephen E., Trahan, Michael W., and Wagner, John S. Tue .
"Particle analysis using laser ablation mass spectroscopy". United States. https://www.osti.gov/servlets/purl/1174488.
@article{osti_1174488,
title = {Particle analysis using laser ablation mass spectroscopy},
author = {Parker, Eric P. and Rosenthal, Stephen E. and Trahan, Michael W. and Wagner, John S.},
abstractNote = {The present invention provides a method of quickly identifying bioaerosols by class, even if the subject bioaerosol has not been previously encountered. The method begins by collecting laser ablation mass spectra from known particles. The spectra are correlated with the known particles, including the species of particle and the classification (e.g., bacteria). The spectra can then be used to train a neural network, for example using genetic algorithm-based training, to recognize each spectra and to recognize characteristics of the classifications. The spectra can also be used in a multivariate patch algorithm. Laser ablation mass specta from unknown particles can be presented as inputs to the trained neural net for identification as to classification. The description below first describes suitable intelligent algorithms and multivariate patch algorithms, then presents an example of the present invention including results.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2003},
month = {9}
}
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