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

创新-技术转化伙伴关系（PFI-TT）项目开发可减少粮食损失/浪费的传感器。奶酪行业的大部分食物浪费发生在加工过程中，为了减少损失，奶酪/牛奶会被丢弃。这种新型传感器可以提供奶酪机械行为的实时信息，从而自动调整切割参数，从而减少切割损失。此外，为每包奶酪收集的机械性能数据将创建一个详细的、有标签的数据库，这对奶酪制造商和零售商来说非常有价值。例如，奶酪制造商可以使用该数据库来推断奶酪质地和成分的饮食、季节和地区变化。从长远来看，这将促进标准化，并减少由于奶酪制作过程中的变化而造成的修剪损失。该数据库将有助于质量控制和包装一致性，并提高零售阶段的客户满意度。该传感器可以扩展到其他部分的消费包装商品，如瓜分水果、蔬菜、肉类和谷物棒。此外，受训人员将通过这个项目发展技术和创业技能。该项目将解决奶酪加工过程中的边角损耗问题，这一问题每年可能导致高达30%的收入损失和数十亿磅的奶酪/牛奶损失。将制作一种传感器的原型，该传感器利用切割过程中发出的表面波中包含的信息来确定材料的行为。目标是：(1)开发一个原型传感器，(2)在控制良好的奶酪替代品上测试传感器，(3)在普通奶酪上测试传感器。原型传感器将使用低成本的光学传感器开发，并将对多点激光振动测量进行验证。最初的传感器测试将在带有可变形填料的明胶上完成。之所以选择这种奶酪替代品，是因为之前的研究使用，因为它可以严格控制材料的行为，以确认传感器的输出。随后的传感器测试将集中在占美国奶酪消费量三分之二的奶酪上（淡味/浓味切达奶酪、豪达奶酪和马苏里拉奶酪）。预期的技术成果将是一个工作原型传感器和对表面波和奶酪特性之间关系的透彻理解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.