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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 #353706

Research Project: Develop Rapid Optical Detection Methods for Food Hazards

Location: Quality and Safety Assessment Research Unit

Title: Identification of Campylobacter with hyperspectral microscope imaging

item Park, Bosoon
item Eady, Matthew

Submitted to: International Association for Spectral Imaging
Publication Type: Abstract Only
Publication Acceptance Date: 4/13/2018
Publication Date: N/A
Citation: N/A

Interpretive Summary: n/a

Technical Abstract: Campylobacter is the major group of bacteria responsible for foodborne gastroenteritis in humans worldwide. Most of the cases of campylobacteriosis have revealed their poultry origins so that various detection methods have been employed from the farm to processing levels. Especially Campylobacter jejuni is the most common cause of bacterial foodborne illness in the United States. The Center for Disease Control and Prevention (CDC) estimates that Campylobacter causes approximately 845,000 illnesses in the US each year. Specifically, over 6,000 cases of Campylobacter infection were reported in 2009. Also, European Food Safety Authority (EFSA) estimates the cost of campylobacteriosis in the EU to be approximately EUR 2.4 billion per year so that food safety is focused on controlling Campylobacter infections. Current methods for isolating and detecting Campylobacter from foods are culture-based techniques using several selective agars designed to isolate Campylobacter colonies. In addition, several immunological and molecular techniques are commercially available for the detection and identification of Campylobacter. However, most significant drawbacks of current methods are laborious for process, time-consuming for results, and expensive to use. Recently, hyperspectral microscope imaging (HMI) has demonstrated the potential to differentiate gram-positive from gram-negative bacteria with high accuracy (>99%) and also identified Salmonella serotypes (>95%) as well as Staphylococcus (>94%). Herein we expand HMI methods we developed to identify Campylobacter serovars. The objective of this research is to develop rapid methods to identify and detect Campylobacter serotypes with hyperspectral microscope imaging using scattering intensity from live bacterial cells.