Biological agent detection based on principal component analysis

This paper presents an algorithm, based on principal component analysis for the detection of biological threats using General Dynamics Canada's 4WARN Sentry 3000 biodetection system. The proposed method employs a statistical method for estimating background biological activity so as to make the algorithm adaptive to varying background situations. The method attempts to characterize the pattern of change that occurs in the fluorescent particle counts distribution and uses the information to suppress false-alarms. The performance of the method was evaluated using a total of 68 tests including 51 releases of Bacillus Globigii (BG), six releases of BG in the presence of obscurants, six releases of obscurants only, and five releases of ovalbumin at the Ambient Breeze Tunnel Test facility, Battelle, OH. The peak one-minute average concentration of BG used in the tests ranged from 10 - 65 Agent Containing Particles per Liter of Air (ACPLA). The obscurants used in the tests included diesel smoke, white grenade smoke, and salt solution. The method successfully detected BG at a sensitivity of 10 ACPLA and resulted in an overall probability of detection of 94% for BG without generating any false-alarms for obscurants at a detection threshold of 0.6 on a scale of 0 to 1. Also, the method successfully detected BG in the presence of diesel smoke and salt water fumes. The system successfully responded to all the five ovalbumin releases with noticeable trends in algorithm output and alarmed for two releases at the selected detection threshold.