4.6 Article

Battery-Free and Noninvasive Estimation of Food pH and CO2 Concentration for Food Monitoring Based on Pressure Measurement

期刊

SENSORS
卷 20, 期 20, 页码 -

出版社

MDPI
DOI: 10.3390/s20205853

关键词

near-field communication; battery-free food monitoring; energy harvesting; smart sensor tag; food pH extraction

资金

  1. National Researcher Foundation of Korea (NRF) - Korea government (MSIT) [2019R1A2C1089139]
  2. National Research Foundation of Korea [2019R1A2C1089139] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

In this paper, we developed a battery-free system that can be used to estimate food pH level and carbon dioxide (CO2) concentration in a food package from headspace pressure measurement. While being stored, food quality degrades gradually as a function of time and storage conditions. A food monitoring system is, therefore, essential to prevent the detrimental problems of food waste and eating spoilt food. Since conventional works that invasively measure food pH level and CO2 concentration in food packages have shown several disadvantages in terms of power consumption, system size, cost, and reliability, our study proposes a system utilizing package headspace pressure to accurately and noninvasively extract food pH level and CO2 concentration, which reflection food quality. To read pressure data in the food container, a 2.5 cm x 2.5 cm smart sensor tag was designed and integrated with near-field communication (NFC)-based energy harvesting technology for battery-free operation. To validate the reliability of the proposed extraction method, various experiments were conducted with different foods, such as pork, chicken, and fish, in two storage environments. The experimental results show that the designed system can operate in a fully passive mode to communicate with an NFC-enabled smartphone. High correlation coefficients of the headspace pressure with the food pH level and the headspace CO2 concentration were observed in all experiments, demonstrating the ability of the proposed system to estimate food pH level and CO2 concentration with high accuracy. A linear regression model was then trained to linearly fit the sensor data. To display the estimated results, we also developed an Android mobile application with an easy-to-use interface.

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