Dual-Mode Batteryless Ammonia Sensor Using Polyvinyl Alcohol-Reinforced Clitoria ternatea Anthocyanin With Graphene Nanoplatelets for Enhanced Food Quality Monitoring

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Sensors Journal Pub Date : 2024-04-30 DOI:10.1109/JSEN.2024.3392954
Thiresamary Kurian;Chun-Hui Tan;Pei-Song Chee;Vinod Ganesan
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Abstract

In the food industry, confusion stemming from expiration and date labels contributes to unnecessary food waste, underscoring the growing need for innovative food freshness sensors. This study presents a novel, cost-effective, and environmentally friendly dual-mode ammonia sensor tailored for real-time quality monitoring of protein-rich food products. Utilizing naturally occurring anthocyanin extracted from Clitoria ternatea (CT) and reinforced with polyvinyl alcohol (PVA) in a paper-based colorimetric system, the sensor demonstrates heightened sensitivity to ammonia gas, a key indicator of spoilage in protein-rich foods. Integration of a graphene nanoplatelets (GNPs) layer enables additional resistive gas sensing capabilities. The practicality and versatility of the fabricated sensor are enhanced by integrating near-field communication (NFC) technology, which facilitates batteryless and wireless sensing response transmission. The fabrication process of the sensor involves a straightforward, low-temperature solution route utilizing dip-coating and brush-coating methods. The incorporation of PVA significantly amplifies the colorimetric response, evidenced by a 44% increase in total color change compared to non-PVA reinforced sensors. This augmentation results in a more pronounced color change, which is readily discernible to the naked eye. The developed dual-mode sensor, equipped with NFC, is successfully applied to monitor shrimp freshness, demonstrating distinct color changes and NFC tag readability in response to ammonia release during spoilage. With its attributes of cost-effectiveness, environmental friendliness, simplicity, and wireless capabilities, this sensor offers a promising solution for widespread adoption in the food industry. This work contributes to advancing sensor technology, providing a versatile tool to ensure the quality and safety of perishable goods.
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使用石墨烯纳米颗粒的聚乙烯醇增强型虎耳草花青素的双模无电池氨气传感器用于增强食品质量监测
在食品工业中,保质期和日期标签引起的混淆造成了不必要的食品浪费,因此对创新型食品新鲜度传感器的需求与日俱增。本研究提出了一种新颖、经济、环保的双模氨传感器,专为实时监测富含蛋白质的食品质量而量身定制。该传感器利用从三尖杉(Clitoria ternatea,CT)中提取的天然花青素,并用聚乙烯醇(PVA)在纸质比色系统中进行增强,从而提高了对氨气的灵敏度,而氨气是富含蛋白质的食品变质的关键指标。集成石墨烯纳米板(GNPs)层可增强电阻式气体传感能力。通过集成近场通信(NFC)技术,可实现无电池和无线传感响应传输,从而增强了所制造传感器的实用性和多功能性。该传感器的制造过程采用了直接的低温溶液工艺,利用了浸涂和刷涂方法。与非 PVA 增强型传感器相比,PVA 的加入大大增强了比色反应,总颜色变化增加了 44%。这种增强使颜色变化更加明显,肉眼很容易辨别。所开发的双模式传感器配备了 NFC 功能,成功地应用于监测虾的新鲜度,在虾变质过程中氨气释放时显示出明显的颜色变化和 NFC 标签的可读性。这种传感器具有成本效益高、环保、简便和无线功能等特点,为食品工业的广泛应用提供了一种前景广阔的解决方案。这项工作有助于推动传感器技术的发展,为确保易腐货物的质量和安全提供了一种多功能工具。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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