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Proceedings Paper

Digital microbiology: detection and classification of unknown bacterial pathogens using a label-free laser light scatter-sensing system
Author(s): Bartek Rajwa; M. Murat Dundar; Ferit Akova; Valery Patsekin; Euiwon Bae; Yanjie Tang; J. Eric Dietz; E. Daniel Hirleman; J. Paul Robinson; Arun K. Bhunia
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Paper Abstract

The majority of tools for pathogen sensing and recognition are based on physiological or genetic properties of microorganisms. However, there is enormous interest in devising label-free and reagentless biosensors that would operate utilizing the biophysical signatures of samples without the need for labeling and reporting biochemistry. Optical biosensors are closest to realizing this goal and vibrational spectroscopies are examples of well-established optical label-free biosensing techniques. A recently introduced forward-scatter phenotyping (FSP) also belongs to the broad class of optical sensors. However, in contrast to spectroscopies, the remarkable specificity of FSP derives from the morphological information that bacterial material encodes on a coherent optical wavefront passing through the colony. The system collects elastically scattered light patterns that, given a constant environment, are unique to each bacterial species and/or serovar. Both FSP technology and spectroscopies rely on statistical machine learning to perform recognition and classification. However, the commonly used methods utilize either simplistic unsupervised learning or traditional supervised techniques that assume completeness of training libraries. This restrictive assumption is known to be false for real-life conditions, resulting in unsatisfactory levels of accuracy, and consequently limited overall performance for biodetection and classification tasks. The presented work demonstrates preliminary studies on the use of FSP system to classify selected serotypes of non-O157 Shiga toxin-producing E. coli in a nonexhaustive framework, that is, without full knowledge about all the possible classes that can be encountered. Our study uses a Bayesian approach to learning with a nonexhaustive training dataset to allow for the automated and distributed detection of unknown bacterial classes.

Paper Details

Date Published: 16 May 2011
PDF: 9 pages
Proc. SPIE 8029, Sensing Technologies for Global Health, Military Medicine, Disaster Response, and Environmental Monitoring; and Biometric Technology for Human Identification VIII, 80290C (16 May 2011); doi: 10.1117/12.884541
Show Author Affiliations
Bartek Rajwa, Purdue Univ. (United States)
M. Murat Dundar, Indiana Univ.-Purdue Univ. Indianapolis (United States)
Ferit Akova, Indiana Univ.-Purdue Univ. Indianapolis (United States)
Valery Patsekin, Purdue Univ. (United States)
Euiwon Bae, Purdue Univ. (United States)
Yanjie Tang, Purdue Univ. (United States)
J. Eric Dietz, Purdue Univ. (United States)
E. Daniel Hirleman, Univ. of California, Merced (United States)
J. Paul Robinson, Purdue Univ. (United States)
Arun K. Bhunia, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 8029:
Sensing Technologies for Global Health, Military Medicine, Disaster Response, and Environmental Monitoring; and Biometric Technology for Human Identification VIII
B. V. K. Vijaya Kumar; Salil Prabhakar; Arun A. Ross; Sárka O. Southern; Kevin N. Montgomery; Carl W. Taylor; Bernhard H. Weigl, Editor(s)

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