Classification of Shiga toxin-producing escherichia coli (STEC) serotypes with hyperspectral microscope imagery

Bosoon Park; William R. Windham; Scott R. Ladely; Prudhvi Gurram; Heesung Kwon; Seung-Chul Yoon; Kurt C. Lawrence; Neelam Narang; William C. Cray

doi:10.1117/12.919695

4 May 2012 Classification of Shiga toxin-producing escherichia coli (STEC) serotypes with hyperspectral microscope imagery

Bosoon Park, William R. Windham, Scott R. Ladely, Prudhvi Gurram, Heesung Kwon, Seung-Chul Yoon, Kurt C. Lawrence, Neelam Narang, William C. Cray

Author Affiliations +

Proceedings Volume 8369, Sensing for Agriculture and Food Quality and Safety IV; 83690L (2012) https://doi.org/10.1117/12.919695
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

Non-O157:H7 Shiga toxin-producing Escherichia coli (STEC) strains such as O26, O45, O103, O111, O121 and O145 are recognized as serious outbreak to cause human illness due to their toxicity. A conventional microbiological method for cell counting is laborious and needs long time for the results. Since optical detection method is promising for realtime, in-situ foodborne pathogen detection, acousto-optical tunable filters (AOTF)-based hyperspectral microscopic imaging (HMI) method has been developed for identifying pathogenic bacteria because of its capability to differentiate both spatial and spectral characteristics of each bacterial cell from microcolony samples. Using the AOTF-based HMI method, 89 contiguous spectral images could be acquired within approximately 30 seconds with 250 ms exposure time. From this study, we have successfully developed the protocol for live-cell immobilization on glass slides to acquire quality spectral images from STEC bacterial cells using the modified dry method. Among the contiguous spectral imagery between 450 and 800 nm, the intensity of spectral images at 458, 498, 522, 546, 570, 586, 670 and 690 nm were distinctive for STEC bacteria. With two different classification algorithms, Support Vector Machine (SVM) and Sparse Kernel-based Ensemble Learning (SKEL), a STEC serotype O45 could be classified with 92% detection accuracy.

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Bosoon Park, William R. Windham, Scott R. Ladely, Prudhvi Gurram, Heesung Kwon, Seung-Chul Yoon, Kurt C. Lawrence, Neelam Narang, and William C. Cray "Classification of Shiga toxin-producing escherichia coli (STEC) serotypes with hyperspectral microscope imagery", Proc. SPIE 8369, Sensing for Agriculture and Food Quality and Safety IV, 83690L (4 May 2012); https://doi.org/10.1117/12.919695

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