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ARS Home » Southeast Area » Athens, Georgia » U.S. National Poultry Research Center » Quality and Safety Assessment Research Unit » Research » Publications at this Location » Publication #314195

Title: AOTF hyperspectral microscope imaging for foodborne bacteria detection

item Park, Bosoon

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 6/5/2015
Publication Date: 9/29/2015
Citation: Park, B. 2015. AOTF hyperspectral microscope imaging for foodborne bacteria detection. In: Park, B., Lu, R., editors. Hyperspectral Imaging Technology in Food and Agriculture. New York, NY: Springer Science and Business Media. p. 359-390.

Interpretive Summary: none

Technical Abstract: Food safety is an important public health issue worldwide. Researchers have developed many different methods for detecting foodborne pathogens; however, most technologies currently being used have limitations, in terms of speed, sensitivity and selectivity, for practical use in the food industry. Acousto-optic tunable filter (AOTF)-based hyperspectral microscope imaging (HMI) is an optical method for rapidly identifying foodborne pathogenic bacteria at the single cell level. In conjunction with dark-field illumination, HMI method is able to acquire spectral signatures from the scattering intensity of bacteria cells. Researchers have successfully developed the method to acquire quality hyperspectral microscopic images from various foodborne pathogenic bacteria live cells. From the contiguous spectral images over the visible and near-infrared electromagnetic spectral bands, the spectral scattering signature for different bacterial species can be observed at selected wavelengths. Since scattering peak intensity at various wavelengths depends on bacteria serotypes as well as species, statistical models can be developed to classify different foodborne bacteria. Herein, we introduce a hyperspectral microscope imaging system, immobilization of live bacterial cell for image acquisition, spectral characteristics of bacteria, and classification methods. We demonstrate classification of three bacteria species including Salmonella, Staphylococcus, and Escherichia coli. High classification accuracy is obtained from classification models with scattering intensity data from bacteria cells. The performance of the classification models has been validated with bacterial cultures from food matrices, so that latex agglutination or polymerase chain reaction (PCR) tests can confirm positively identified colonies of bacterial samples using a rapid hyperspectral microscope imaging method.