Rapid identification and classification of Campylobacter spp. using laser optical scattering technology

Area of Science:

• Microbiology
• Biosensor Technology
• Food Safety

Background:

• Campylobacter infections pose a significant public health risk, primarily caused by Campylobacter jejuni and Campylobacter coli.
• Accurate detection of Campylobacter in food is challenging due to complex matrices and the organism's fastidious nature.
• Existing methods often require lengthy procedures and can be hindered by food sample interference.

Purpose of the Study:

• To develop and validate a novel biosensor-based detection method for rapid, high-throughput screening of Campylobacter species.
• To differentiate Campylobacter from other common foodborne pathogens using optical scattering patterns.
• To assess the applicability of the developed biosensor in real food samples.

Main Methods:

• Development of BARDOT (Bacterial Rapid Detection using Optical scattering Technology) for analyzing light scatter patterns of bacterial colonies.
• Utilized image pattern characterization and principal component analysis (PCA) on 6909 bacterial colonies.
• Tested BARDOT on mixed cultures and spiked/naturally contaminated chicken samples, with verification by real-time PCR.

Main Results:

• BARDOT demonstrated distinct light scatter patterns for Campylobacter jejuni and Campylobacter coli compared to Salmonella spp., E. coli O157:H7, and Listeria monocytogenes.
• The biosensor achieved 85% accuracy in differentiating Campylobacter from other pathogens in mixed cultures.
• BARDOT successfully identified Campylobacter isolates from real food samples (ground and fresh chicken), especially when combined with passive filtration and PCR.

Conclusions:

• BARDOT is a promising novel biosensor for the rapid and accurate detection of Campylobacter in food.
• The technology overcomes limitations of traditional methods by enabling high-throughput screening without disrupting colonies.
• Passive filtration enhances BARDOT's performance in complex food matrices, improving overall detection reliability.