An Integrated Food Freshness Sensor Array System Augmented by a Metal–Organic Framework Mixed-Matrix Membrane and Deep Learning,ACS Sensors

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An Integrated Food Freshness Sensor Array System Augmented by a Metal–Organic Framework Mixed-Matrix Membrane and Deep Learning
ACS Sensors ( IF 8.2 ) Pub Date : 2022-07-14 , DOI: 10.1021/acssensors.2c00255
Peihua Ma ₁ , Wenhao Xu ₂ , Zi Teng _{1,

3} , Yaguang Luo ₃ , Cheng Gong ₄ , Qin Wang ₁

Affiliation

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.

中文翻译：

由金属-有机框架混合矩阵膜和深度学习增强的集成食品新鲜度传感器阵列系统

目前在食品市场上流行的静态标签面临挑战，因为假设食品仅经历有限范围的预定条件，这会导致安全风险增加或易腐烂食品的浪费。因此，已经开发了用于实时测量食品新鲜度的集成系统，以提高食品供应的可靠性、安全性和可持续性。然而，这些系统受到灵敏度和准确性差的限制。在这里，结合了金属-有机框架混合基质膜和深度学习技术来应对这些挑战。用六种显色指示剂浸渍UiO-66-OH和聚乙烯醇以制备传感器阵列复合材料。传感器在暴露于不同 pH 值的氨后会发生颜色变化。获得了 80 ppm 三甲胺的检测限，这在食品工业中实际上是可以接受的。四个最先进的深度卷积神经网络被应用于识别颜色变化，赋予其高精度的新鲜度估计。WISeR-50算法对鸡肉新鲜度估计的模拟测试达到了98.95%的准确率。此外，应用 3D 打印制造模具以实现规模化生产，并在概念上构建了便携式食品新鲜度检测平台。这种方法有可能推进综合和实时的食品新鲜度估计。赋予其高精度的新鲜度估计。WISeR-50算法对鸡肉新鲜度估计的模拟测试达到了98.95%的准确率。此外，应用 3D 打印制造模具以实现规模化生产，并在概念上构建了便携式食品新鲜度检测平台。这种方法有可能推进综合和实时的食品新鲜度估计。赋予其高精度的新鲜度估计。WISeR-50算法对鸡肉新鲜度估计的模拟测试达到了98.95%的准确率。此外，应用 3D 打印制造模具以实现规模化生产，并在概念上构建了便携式食品新鲜度检测平台。这种方法有可能推进综合和实时的食品新鲜度估计。

更新日期：2022-07-14

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