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Battery-Free and Noninvasive Estimation of Food pH and CO(2) Concentration for Food Monitoring Based on Pressure Measurement

In this paper, we developed a battery-free system that can be used to estimate food pH level and carbon dioxide (CO(2)) 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 monito...

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Detalles Bibliográficos
Autores principales: Nguyen, Thanh-Binh, Nguyen, Trung-Hau, Chung, Wan-Young
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7589979/
https://www.ncbi.nlm.nih.gov/pubmed/33081188
http://dx.doi.org/10.3390/s20205853
Descripción
Sumario:In this paper, we developed a battery-free system that can be used to estimate food pH level and carbon dioxide (CO(2)) 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 CO(2) 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 CO(2) concentration, which reflection food quality. To read pressure data in the food container, a 2.5 cm × 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 CO(2) concentration were observed in all experiments, demonstrating the ability of the proposed system to estimate food pH level and CO(2) 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.