Recombinant Antibody-Based Biosensor for Rapid Detection of Salmonella in Foods

Non-Technical Summary: Due to the prevalence of Salmonella species in the food supply, new technologies for rapid and reliable monitoring are needed to facilitate implementation of preventive measurements and to reduce their impact upon human health. The purpose of this project is to develop an innovative biosensor utilizing recombinant antibodies for rapid detection and identification of Salmonella in foods. <P> Approach: A biosensor platform that incorporates an integrated miniature surface plasmon resonance (SPR) liquid sensor will be developed using the recombinant antibodies specific to Salmonella. The sensor will contain a near-infrared light emitting diode, a gold sensing surface, and a linear array of silicon photodiodes. All of the components will be immobilized in an optically transparent material. The sensor will be interfaced to an A/D converter that processes the signal and transmits it to a PC for display and analysis. Surface immobilization procedures based on biotin/avidin chemistry will be explored to attach antibodies to the gold sensing surface. The overall performance of the sensor, including specificity, sensitivity and reproducibility, will be characterized. Linearity of the SPR sensor response to various buffer compositions will be evaluated over a range of concentrations and repeatability of the sensor response will be assessed with repeat injection over a period of time. Control binding experiments will be performed to correct possible non-specific binding interactions and the assay conditions will be optimized to minimize the deviation in the baseline signal of detection. Reproducibility of protein binding using the biotin/avidin immobilization scheme and the sensor's limit of detection will be determined. To further validate the performance of the sensor, we will conduct validation tests on raw and ready-to-eat meats and poultry products. Blind studies will be conducted with spiked samples to simulate the actual processed samples. Performance of the sensor will be described in terms of precision, and accuracy, which are two indicators of data quality. Precision, the reproducibility under a given set of conditions, will be determined, and standard deviation (SD) and relative standard deviation (RSD) will be used to assess precision for quantitative data. Accuracy, the closeness of the sensor's measurements to known values, will be evaluated in terms of the percentage of samples that agreed with, above (i.e., biased high), and below (i.e., biased low) the known value.