Advanced sensors to detect food freshness

每年，全球农业生产1.931亿吨肉类、1.4亿吨海产品和1.32亿吨鸡肉供人类食用。由于供应链效率低下、供过于求和缺乏标准化的质量控制协议，这需要耗费大量资源。目前，微生物实验室的数据既昂贵又稀少，这使得食品行业一直采用一刀切、规避风险的方法来监测食品的新鲜度，导致了过多的、本可避免的食物浪费和碳排放。黑熊正在解决冷链的浪费问题，通过专利的TRL7原型传感器技术实时监测食物腐败。我们正在申请专利的纸质电子气体传感器（PEGS）是一种高灵敏度、低成本的数据捕获技术，可测量食物变质气体（胺、二氧化碳、挥发性有机化合物）。传感器以数字方式收集腐败数据，并将其传输到云端。相应的黑熊App可以让用户实时了解食品变质数据，并做出影响冷链监控的决策。传感鲁棒性的这一步骤变化将使供应链能够使用我们的传感器为决策提供信息，例如包装类型，定位和堆叠蛋白质的方式，冷却器温度，拒收货物和生态标签。在这个项目中，blackbear将通过一种新型的先进传感器收集腐败数据，该传感器对包括氨/三甲胺/二氧化碳在内的水溶性腐败气体具有高灵敏度，以提高腐败测量和人工智能预测腐败建模的准确性。随着时间的推移，我们的传感器将取代“最佳猜测”微生物测试过程，催化更长的保质期，并告知行业冷链效率低下，以便有针对性地进行改进。消除冷链不一致将减少供应商为弥补浪费而生产的新鲜食品的供过于求。我们相信这将为英国食品行业每年节省11亿英镑。该项目是可持续供应链的天然垫脚石，关键的食品质量参数在没有人为干预的情况下自动实时报告。

Every year, the global agricultural industry produces 193.1m tonnes of meat, 140m tonnes of seafood and 132m tonnes of chicken for human consumption. This takes tremendous resources, due to supply chain inefficiencies, oversupply and a lack of standardised quality control protocol. Microbiology lab data is currently expensive and sparse, perpetuating one-size-fits-all and risk averse approaches to monitoring food freshness in the industry, resulting in excessive, avoidable food waste and carbon emissions. BlakBear is solving the cold-chain's waste problem with real-time food spoilage monitoring via a patented, prototyped TRL7 sensor technology. Our patent-pending paper-based electrical gas sensors (PEGS) are a highly sensitive, low-cost data capture technology, which measures food spoilage gases (Amines, CO2, VOCs). Sensors collect spoilage data digitally and transmit this to the Cloud. The corresponding BlakBear App allows users to understand food spoilage data in real-time and make decisions that impact the cold chain monitoring. This step change in sensing robustness will enable supply chains to use our sensors to inform decision-making, such as packaging type, the way to position and stack protein, chiller temperatures, rejection of shipments and eco-labelling. In this project BlakBear will collect spoilage data via a new and novel advanced sensor with high sensitivity to water-soluble spoilage gases including ammonia/trimethylamine/carbon dioxide to improve the accuracy of spoilage measurements and AI predictive spoilage modelling. In time our sensors will replace "best guess" microbiology testing processes, catalyse longer shelf-life and inform industry on cold-chain inefficiencies so that targeted improvements can be made. Removing cold chain inconsistencies will reduce the oversupply of fresh food that suppliers produce to compensate for waste. We believe this could save the UK food industry alone £1.1bn/year. This project is a natural stepping stone in sustainable supply chains, where crucial food quality parameters are automatically reported in real-time without human intervention.