COLLABORATIVE RESEARCH: Disposable All-Graphene Microfluidic Biosensor System for Real-Time Foodborne Pathogen Detection in Food Processing Facilities

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Disease-causing bacteria found in food causes 48 million illnesses and $15.6 billion in health-related costs annually in the U.S. alone. This project will create bacteria testing technology (i.e., sensors) that are cost-effective, rapid, and easy-to-use to ensure wide use in food processing facilities. The sensors will work and look much like a home blood sugar test device that will permit users to test for pathogens from swab samples taken from virtually any surface including equipment and floor drains. Multiple sensors will be developed and connected to the internet so that measurements can be collected and analyzed simultaneously at a central location. Contamination breakouts will therefore be quickly identified and pinpointed so that appropriate corrective measures can be taken prior to the contaminated food reaches the market. Outreach activities will include hands-on exhibit on sensors and mentoring students in Women Explore Engineering Summer Camp.Foodborne pathogen detection in processing facilities is infrequently performed because of the cost ($8-10 per test), time (24-48 h for results), and low sensitivity (~100 CFU/mL detection limits that usually require sample pre-enrichment) associated with current laboratory test techniques. Thus, the sources of pathogen contamination are not determined in a timely fashion prior to the contaminated food reaching the consumer. Field deployable foodborne pathogen biosensors that are low-cost, rapid (a few minutes), and highly sensitive ( 5 CFU/mL detection limits) are highly desirable, but currently do not exist. The objective of the proposed work is to develop effective field deployable biosensors for Salmonella in food processing facility that pose high risk for food contamination (e.g., equipment, work surfaces). Multiple biosensors will be created and used within a food processing facility, while the measured data will be streamed to a central location via the internet. Through the Internet of Things (IoT) paradigm the sensor network will enable simultaneous monitoring of pathogens and sanitation efficacy so that appropriate action can be implemented. The proposed biosensor is expected to exhibit a sensitivity comparable to, if not better than, current laboratory-based Salmonella detection methods. The project specific aims are: Aim 1: Develop a graphene-based microfluidic biosensor system; Aim 2: Biofunctionalize and evaluate the biosensor system for foodborne pathogen detection; Aim 3: Evaluate data from multiple biosensors within a food processing facility via an IoT paradigm. The graphene-based biosensor and corresponding microfluidics system uses inkjet printing and rapid laser-pulse annealing to create graphene surfaces with high electrical conductivity, tunable hydrophobicity, and nanostructured morphologies that can operate synergistically to detect Salmonella with a high sensitivity and without the need for pre-enrichment techniques. The graphene electrodes will be biofunctionalized with aptamers that have binding affinities similar to monoclonal antibodies. The aptamers selectivity to the targeted Salmonella strains will be evaluated within the presence of other potential interferents (e.g., other gram-negative bacteria) in buffer, chicken broth, carcass rinsate, and swab samples. The biosensor will be optimized to negate false positives and false negatives. If the aptamers are not sufficiently sensitive or selective than monoclonal antibodies (e.g. Anti-Salmonella) will be used instead. Small-scale food processing facilities at TAMU, ISU, and AES Controls (industry collaborator) will be used to help validate the sensor system in a food processing setting. Also, the Virtual Reality Applications Center at ISU (Co-PI is the co-director) will directly work in developing this ad-hoc network. Outreach activities the development of an interactive exhibit that displays how nanoscale and microscale patterning induces hydrophobicity, IoT-based learning modules for underrepresented minority students, hands-on demonstrations on biosensor design and foodborne pathogen detection and mentoring young women in the Women Explore Engineering (WEE) summer camp.