Advanced sensors to detect food freshness

全球农业每年生产1.931亿吨肉类、1.4亿吨海鲜和1.32亿吨鸡肉供人类食用。由于供应链效率低下、供过于求以及缺乏标准化的质量控制协议，这需要投入大量资源。微生物学实验室数据目前昂贵而稀少，使该行业监测食品新鲜度的一刀切和规避风险的方法永久化，导致过度的、可避免的食品浪费和碳排放。BlakBear正在通过获得专利的原型TRL7传感器技术，通过实时食品腐败监测来解决冷链的浪费问题。我们正在申请专利的纸基电子气体传感器(PEGS)是一种高灵敏度、低成本的数据捕获技术，可测量食品腐败气体(胺、二氧化碳、VOCs)。传感器以数字方式收集损坏数据，并将其传输到云。对应的BlakBear App可以让用户实时了解食品变质数据，做出影响冷链监测的决策。传感稳定性的这一步变化将使供应链能够使用我们的传感器来为决策提供信息，例如包装类型、蛋白质定位和堆叠的方式、冷冻机温度、拒绝发货和生态标签。在这个项目中，BlakBear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AI预测腐败模型的准确性。随着时间的推移，我们的传感器将取代“最佳猜测”的微生物测试过程，促进更长的保质期，并向行业通报冷链效率低下的情况，以便进行有针对性的改进。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供应过剩。我们相信，这可以为英国食品业每年节省11亿英镑。该项目是可持续供应链的天然垫脚石，在可持续供应链中，关键的食品质量参数自动实时报告，无需人工干预。

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.