Share Email Print
cover

Proceedings Paper

Hyperspectral imaging for detection of non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups on spread plates of mixed cultures
Author(s): Seung Chul Yoon; William R. Windham; Scott Ladely; Gerald W. Heitschmidt; Kurt C. Lawrence; Bosoon Park; Neelam Narang; William C. Cray
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

We investigated the feasibility of visible and near-infrared (VNIR) hyperspectral imaging for rapid presumptive-positive screening of six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) on spread plates of mixed cultures. Although the traditional culture method is still the "gold standard" for presumptive-positive pathogen screening, it is time-consuming, labor-intensive, not effective in testing large amount of food samples, and cannot completely prevent unwanted background microflora from growing together with target microorganisms on agar media. A previous study was performed using the data obtained from pure cultures individually inoculated on spot and/or spread plates in order to develop multivariate classification models differentiating each colony of the six non-O157 STEC serogroups and to optimize the models in terms of parameters. This study dealt with the validation of the trained and optimized models with a test set of new independent samples obtained from colonies on spread plates of mixed cultures. A new validation protocol appropriate to a hyperspectral imaging study for mixed cultures was developed. One imaging experiment with colonies obtained from two serial dilutions was performed. A total of six agar plates were prepared, where O45, O111 and O121 serogroups were inoculated into all six plates and each of O45, O103 and O145 serogroups was added into the mixture of the three common bacterial cultures. The number of colonies grown after 24-h incubation was 331 and the number of pixels associated with the grown colonies was 16,379. The best model found from this validation study was based on pre-processing with standard normal variate and detrending (SNVD), first derivative, spectral smoothing, and k-nearest neighbor classification (kNN, k=3) of scores in the principal component subspace spanned by 6 principal components. The independent testing results showed 95% overall detection accuracy at pixel level and 97% at colony level. The developed model was proven to be still valid even for the independent samples although the size of a test set was small and only one experiment was performed. This study was an important first step in validating and updating multivariate classification models for rapid screening of ground beef samples contaminated by non-O157 STEC pathogens using hyperspectral imaging.

Paper Details

Date Published: 16 May 2012
PDF: 10 pages
Proc. SPIE 8369, Sensing for Agriculture and Food Quality and Safety IV, 836909 (16 May 2012); doi: 10.1117/12.919631
Show Author Affiliations
Seung Chul Yoon, USDA Agricultural Research Service (United States)
William R. Windham, USDA Agricultural Research Service (United States)
Scott Ladely, 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)
Neelam Narang, USDA Food Safety and Inspection Service (United States)
William C. Cray, USDA Food Safety and Inspection Service (United States)


Published in SPIE Proceedings Vol. 8369:
Sensing for Agriculture and Food Quality and Safety IV
Moon S. Kim; Shu-I Tu; Kuanglin Chao, Editor(s)

© SPIE. Terms of Use
Back to Top