Quantitative and qualitative analysis of spectral data obtained from an integrated fiber optic fluorometer and cone penetrometer
- Naval Command, Control, Ocean Surveillance Center, San Diego, CA (United States). Research, Development, Test and Evaluation Division
The use of an artificial neural network based method for providing rapid on-line analysis of fluorescence emission spectra is described. The method serves to generate a real time interpretation of spectral data acquired in situ by the Navy`s Site Characterization and Analysis Penetrometer System (SCAPS), an integrated optical fiber fluorometer/cone penetrometer site investigation tool currently being used to delineate subsurface petroleum contamination at hazardous waste sites. Neural network pattern recognition allows for simultaneous qualitative and quantitative analysis of raw spectral data. Quantitative estimation and speciation of different fresh and weathered petroleum products is achieved even in the presence of strong spectral interferences, non-linear response curves, and uncertain matrices. The method also provides for the rejection of spectral interferences arising from natural fluorophores, thereby minimizing the possibility of reporting false positive results. Use of neural processing enables maximal utilization of available spectral and matrix information. By simultaneously taking into account the contribution of multiple input variables, the neural algorithm is able to achieve an optimal solution, defined by the accuracy, precision, sensitivity, and selectivity of the fluorescence sensor itself.
- OSTI ID:
- 231003
- Report Number(s):
- CONF-950209-; TRN: IM9623%%313
- Resource Relation:
- Conference: 4. international symposium on field screening methods for hazardous wastes and toxic chemicals, Las Vegas, NV (United States), 22-24 Feb 1995; Other Information: PBD: 1995; Related Information: Is Part Of Field screening methods for hazardous wastes and toxic chemicals. VIP-47, Volume 1; PB: 733 p.
- Country of Publication:
- United States
- Language:
- English
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