PFI-TT: Prototyping a Electromechanical Sensor to Reduce Cheese Trim Losses

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This Partnerships for Innovation - Technology Translation (PFI-TT) project develops sensor that will reduce food loss/waste. Much of the food waste in the cheese industry occurs during processing, with cheese/milk lost to trim losses. The new sensor will reduce trim losses by providing real-time information on the mechanical behavior of the cheese that will allow automated adjustments to cutting parameters. In addition, the mechanical property data gathered for each pack of cheese will create a detailed and labeled database that will be very valuable for cheese makers and retailers. For instance, cheesemakers may use the database to infer dietary, seasonal, and regional variations of cheese texture and constituents. This, in the long run, will promote standardization and reduce trim losses due to variations in cheesemaking. The database will facilitate quality control and pack-to-pack consistency and improve customer satisfaction at the retail stage. The sensor can be extended to other segments of consumer-packaged goods, such as portioned fruits, vegetables, meat, and cereal bars. In addition, trainees will develop technical and entrepreneurial skills through this project. This project will address the challenge of trim losses in cheese processing, which can result in up to 30% lost revenue and several billion pounds of cheese/milk loss annually. A sensor will be prototyped that leverages recent developments in using information contained in surface waves emitted during cutting to determine material behavior. The objectives are: (1) to develop a prototype sensor, (2) to test the sensor on well-controlled cheese substitutes, and (3) to test the sensor on common cheese. The prototype sensor will be developed using low-cost optical sensors, which will undergo validation against multipoint laser vibrometry. Initial sensor testing will be completed on gelatin with deformable fillers. This cheese substitute has been selected because of prior research use and because it allows tight control over material behavior to confirm the sensor output. Subsequent sensor testing will focus on cheeses that account for 2/3 of U.S. cheese consumption (mild/sharp cheddar, Gouda and mozzarella). The anticipated technical outcomes will be a working prototype sensor and a thorough understanding of the relationships between surface waves and cheese properties.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.