Engineering for Food Safety and Quality

NON-TECHNICAL SUMMARY: This project addresses the increasing demand by consumers for fresh-like, healthy, nutritious and safe food. The US food processing industry is continually challenged by this demand. Furthermore, emerging pathogenic microorganisms, tolerant to conventional treatment methods, also create a demand for improved and novel food processes. The industry must constantly redefine technology to assure wholesomeness in processed foods. To do so, new technologies meet the challenge and play a pivotal role in improving the quality of value-added food products. Without this extensive research, it would be difficult for the industry to effectively compete in the global markets. The US food industry requires ready access to the scientific knowledge, well prepared personnel with appropriate skills, and a continuous dialog between academic researchers and industry practitioners. This project outlines collaborations among engineers, food scientists and other experts across the nation to address the needs of the food industry by advancing technologies through research, preparing our future work force through educating the students, and bridging the gap between research and implementation through outreach. The stakeholders impacted by this project include the food industry, federal regulatory agencies, and consumers.

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APPROACH: This project contributes to the NC1023 efforts by focusing on Objective 1: Advancing the fundamental science and application of technologies to ensure safety and improve quality of food products, though utilizing innovative methods to characterize food materials (Obj 1a) and developing mathematical models to enhance understanding of food processes (Obj 1c). Research will focus on the development of nondestructive, noninvasive sensors to measure food quality and safety with sensors based on magnetic resonance spectroscopy and magnetic resonance imaging (MRI) protocols. Work will result in the implementation of real-time in-line sensing systems suitable for fresh produce and process foods. The MR information will be incorporated into mathematical models. The growth in computing power and advancement in these experimental MR methods allow validation of physics-based models in ways not previously feasible. Studies will be performed to compare analytical and numerical models to experimentally obtained spatial and temporal information. Specifically, component and temperature distributions acquired during processing using magnetic resonance imaging (MRI) techniques will be combined with transport and kinetic models. The results of the combined studies will allow the building of advanced models that include variability of materials, reaction kinetics and simultaneous heat and mass transfer. An example of activities in this area has been the collaboration between the New York-Cornell station and the California station for the validation of a heat and mass transfer model during combined convection/microwave heating of a potato product. The goal (or output) of that work was to give processors a model that provides quantitative predictive capabilities. The effectiveness of the outputs, in general, will be evaluated through Objectives 2 and 3. NC 1023 members have extensive experience teaching food processing courses as part of undergraduate education and as extension activities to the food industry. As a group, we will develop and implement learning outcomes for food science and food engineering students and for food industry professionals. The four level Kirkpatrick model will be used to assess program effectiveness. This assessment plan is based on evaluating participant reactions to a program, assessing the extent the participant has advanced his or her skills, measuring differences in the learner's behavior, and evaluating the extent that the program has been successful in terms of improving product quality and safety.