Computational intelligence in bacterial spore detection and identification

Bruno Bosacchi; Manjusha Mehendale; Warren S. Warren; Herschel Rabitz; Marlan O. Scully

doi:10.1117/12.512614

30 December 2003 Computational intelligence in bacterial spore detection and identification

Bruno Bosacchi, Manjusha Mehendale, Warren S. Warren, Herschel Rabitz, Marlan O. Scully

Proceedings Volume 5200, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI; (2003) https://doi.org/10.1117/12.512614
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

Abstract

Optical techniques are very promising for detecting and identifying bacterial spores. They are potentially superior to the existing “wet chemistry” approaches regarding several important features of an effective alarm system, such as speed, in-field use, continuous monitoring, and reliability. In this paper we discuss the role that computational intelligence (CI) can play in the control and optimization of optical experiments, and in the analysis and interpretation of the large amount of data they provide. After a brief discussion of the use of CI in the classification of optical spectra, we introduce the recently proposed FAST CARS (Femtosecond Adaptive Spectroscopic Techniques for Coherent Anti-Stokes Raman Scattering) technique. Here the role of CI is essential: using an adaptive feedback approach based on genetic algorithms, the hardware system evolves and organizes itself to optimize the intensity of the CARS signal.

Citation Download Citation

Bruno Bosacchi, Manjusha Mehendale, Warren S. Warren, Herschel Rabitz, and Marlan O. Scully "Computational intelligence in bacterial spore detection and identification", Proc. SPIE 5200, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI, (30 December 2003); https://doi.org/10.1117/12.512614

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