Data processing: artificial intelligence – Neural network
Reexamination Certificate
1999-05-28
2003-09-09
Starks, Jr., Wilbert L. (Department: 2121)
Data processing: artificial intelligence
Neural network
C356S336000, C422S091000
Reexamination Certificate
active
06618712
ABSTRACT:
BACKGROUND OF THE INVENTION
This invention relates to the field of the detection an identification of biological samples, specifically those which are or can be made airborne.
Timely detection and identification of airborne microorganisms is increasingly important due to the concern about weapons of mass destruction based on biological agents. Infectious environments such as hospitals, and environmental hazards such as “sick” buildings also make the ability to detect and identify airborne microorganisms important.
Traditional methods used to identify bioaerosols generally involve collecting samples on culture media and identifying them via colony growth and response to various agents. See, e.g., Griffiths et al. “The assessment of bioaerosols: a critical review”, Journal of Aerosol Science 25, 1425-1458 (1994). Traditional methods therefore can be unsuitable for time-critical applications. A technique that shows promise involves the use of highly specific reactions such as antigen-antibody reactions or the hybridization of gene probes to DNA or RNA. Another promising technique involves the use of conventional methods of chemical analysis. Single particle techniques using conventional analysis are primarily based on mass spectrometry and optical emission spectrometry. See, e.g., Sinha et al. “Analysis of individual biological particles in air”, Rapid detection and identification of microorganisms pp. 165-192, Nelson ed., VCH Publishers (1985); Sinha et al. “Analysis of individual biological particles by mass spectrometry”, International Journal of Mass Spectrometry Ion Processes 57, 125-133 (1984); Wood et al. “Time-of-Flight Mass-Spectrometry Methods for Real-Time Analysis of Individual Aerosol Particles”, Trends in Analytical Chemistry 17, 346-356 (1998); Hardin “Lasers help identify airborne particles in real-time”, Photonics Spectra 31, pp. 42 (1997); Spengler et al. “Airborne Particle Analysis”, Science 274, pp. 1996 (1996). Spectroscopic methods rely on optical resolution to detect single cells, while mass spectrometric methods rely on particle beam technology to make measurement times shorter than the time between particle events.
Bioaerosol hazards can be posed by unexpected presence of known bioaerosols, and by the presence of unknown bioaerosols. None of the proposed techniques allows robust detection of biaerosols and the classification of unknown bioaerosols based on information concerning known bioaerosols. There accordingly is a need for a method for quickly detecting and identifying bioaerosols, even if the bioaerosol was not previously known.
SUMMARY OF THE INVENTION
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 particles of known species. 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, alone or in combination with the multivariate patch algorithm, 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.
Advantages and novel features will become apparent to those skilled in the art upon examination of the following description or may be learned by practice of the invention. The objects and advantages of the invention may be realized and attained by means of the instrumentalities and combinations particularly pointed out in the appended claims.
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Parker Eric P.
Rosenthal Stephen E.
Trahan Michael W.
Wagner John S.
Grafe V. Gerald
Libman George H.
Sandia Corporation
Starks, Jr. Wilbert L.
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