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Proceedings Paper

Hyperspectral imaging using a color camera and its application for pathogen detection
Author(s): Seung-Chul Yoon; Tae-Sung Shin; Gerald W. Heitschmidt; Kurt C. Lawrence; Bosoon Park; Gary Gamble
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Paper Abstract

This paper reports the results of a feasibility study for the development of a hyperspectral image recovery (reconstruction) technique using a RGB color camera and regression analysis in order to detect and classify colonies of foodborne pathogens. The target bacterial pathogens were the six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) grown in Petri dishes of Rainbow agar. The purpose of the feasibility study was to evaluate whether a DSLR camera (Nikon D700) could be used to predict hyperspectral images in the wavelength range from 400 to 1,000 nm and even to predict the types of pathogens using a hyperspectral STEC classification algorithm that was previously developed. Unlike many other studies using color charts with known and noise-free spectra for training reconstruction models, this work used hyperspectral and color images, separately measured by a hyperspectral imaging spectrometer and the DSLR color camera. The color images were calibrated (i.e. normalized) to relative reflectance, subsampled and spatially registered to match with counterpart pixels in hyperspectral images that were also calibrated to relative reflectance. Polynomial multivariate least-squares regression (PMLR) was previously developed with simulated color images. In this study, partial least squares regression (PLSR) was also evaluated as a spectral recovery technique to minimize multicollinearity and overfitting. The two spectral recovery models (PMLR and PLSR) and their parameters were evaluated by cross-validation. The QR decomposition was used to find a numerically more stable solution of the regression equation. The preliminary results showed that PLSR was more effective especially with higher order polynomial regressions than PMLR. The best classification accuracy measured with an independent test set was about 90%. The results suggest the potential of cost-effective color imaging using hyperspectral image classification algorithms for rapidly differentiating pathogens in agar plates.

Paper Details

Date Published: 13 March 2015
PDF: 10 pages
Proc. SPIE 9405, Image Processing: Machine Vision Applications VIII, 940506 (13 March 2015); doi: 10.1117/12.2083137
Show Author Affiliations
Seung-Chul Yoon, USDA, Agricultural Research Service (United States)
Tae-Sung Shin, USDA, Agricultural Research Service (United States)
Gerald W. Heitschmidt, USDA, Agricultural Research Service (United States)
Kurt C. Lawrence, USDA, Agricultural Research Service (United States)
Bosoon Park, USDA, Agricultural Research Service (United States)
Gary Gamble, USDA, Agricultural Research Service (United States)

Published in SPIE Proceedings Vol. 9405:
Image Processing: Machine Vision Applications VIII
Edmund Y. Lam; Kurt S. Niel, Editor(s)

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