Computational Intelligence in Bacterial Spore Detection & Identification
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.
