A Comparison of Laser Light-Scattering and Analytical Profile Index Systems for Foodborne Bacteria Identification

激光散射法与分析轮廓指数法在食源性细菌鉴定中的比较

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Abstract

Foodborne bacteria pose substantial risks to human health and food safety. Scientists worldwide have shown great interest in the development of rapid, reliable, and cost-effective methods for identifying foodborne bacteria. Among these methods, optical scattering technology (BARDOT) has emerged as the fastest and most efficient technique, offering a unique pattern of scattered light passing through the center of the bacterial colony for identification purposes. In this study, we examined 118 isolates of foodborne pathogenic bacteria, including Escherichia coli, Enterobacter cloacae, Salmonella enterica, Hafnia alvei, and Proteus mirabilis, derived from various food sources. To identify these isolates, we employed Analytical Profile Index (API) systems -specifically API 20E and ID 32E -which rely on biochemical tests, in addition to laser light-scattering technology. In this method, ideal colonies -which exhibited specific characteristics such as a suitable diameter, isolation from neighboring colonies, and a completely circular shape without any irregular edges -were selected to create scatter images. These scatter images revealed a distinct "fingerprint" that could be utilized to differentiate between the species. This "fingerprint" allowed for the successful identification of all isolates belonging to the five species in our current study, achieving 100% identification accuracy. Our findings demonstrated that laser light-scattering technology provided accurate identification in a cost-effective and safe manner. This method eliminated the need to open the plates containing the bacterial colonies, ensuring the colonies remained intact after identification. Furthermore, the laser light-scattering technique proved to be much more rapid compared to the API 20E and ID 32E systems, which were not only significantly more expensive but also time-consuming and labor-intensive.

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