Real-Time Analysis of Individual Airborne Microparticles Using Laser Ablation Mass Spectroscopy and Genetically Trained Neural Networks
We are developing a method for analysis of airborne microparticles based on laser ablation of individual molecules in an ion trap mass spectrometer. Airborne particles enter the spectrometer through a differentially-pumped inlet, are detected by light scattered from two CW laser beams, and sampled by a pulsed excimer laser as they pass through the center of the ion trap electrodes. After the laser pulse, the stored ions are separated by conventional ion trap methods. The mass spectra are then analyzed using genetically-trained neural networks (NNs). A number of mass spectra are averaged to obtain training cases which contain a recognizable spectral signature. Averaged spectra for a bacteria and a non-bacteria are shown to the NNs, the response evaluated, and the weights of the connections between neurodes adjusted by a Genetic Algorithm (GA) such that the output from the NN ranges from 0 for non-bacteria to 1 for bacteria. This process is iterated until the population of the GA converges or satisfies predetermined stopping criteria. Using this type of bipolar training we have obtained generalizing NNs able to distinguish five new bacteria from five new non-bacteria, none of which were used in training the NN.
- Research Organization:
- Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 3289
- Report Number(s):
- SAND99-0150C; ON: DE00003289
- Resource Relation:
- Conference: IFPAC '99 - Thirteenth International Forum Process Analytical Chemistry; San Antonio, TX; 01/24-27/1999
- Country of Publication:
- United States
- Language:
- English
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