LAUSR

The static labels presently prevalent on the food market are confronted with challenges due to the assumption that a food product only undergoes a limited range of predefined conditions, which cause elevated safety risks or waste of perishable food products. Hence, integrated systems for measuring food freshness in real time have been developed for improving the reliability, safety, and sustainability of the food supply. However, these systems are limited by poor sensitivity and accuracy. Here, a metal-organic framework mixed-matrix membrane and deep learning technology were combined to tackle these challenges. UiO-66-OH and polyvinyl alcohol were impregnated with six chromogenic indicators to prepare sensor array composites. The sensors underwent color changes after being exposed to ammonia at different pH values. The limit of detection of 80 ppm for trimethylamine was obtained, which was practically acceptable in the food industry. Four state-of-the-art deep convolutional neural networks were applied to recognize the color change, endowing it with high-accuracy freshness estimation. The simulation test for chicken freshness estimation achieved accuracy up to 98.95% by the WISeR-50 algorithm. Moreover, 3D printing was applied to create a mold for possible scale-up production, and a portable food freshness detector platform was conceptually built. This approach has the potential to advance integrated and real-time food freshness estimation.