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Battery-Free and Noninvasive Estimation of Food pH and CO2 Concentration for Food Monitoring Based on Pressure Measurement.
Nguyen, Thanh-Binh; Nguyen, Trung-Hau; Chung, Wan-Young.
Afiliação
  • Nguyen TB; Department of Electronic Engineering, Pukyong National University, Busan 48513, Korea.
  • Nguyen TH; Faculty of Applied Science, Ho Chi Minh City University of Technology-Vietnam National University, Ho Chi Minh City 72506, Vietnam.
  • Chung WY; Department of Electronic Engineering, Pukyong National University, Busan 48513, Korea.
Sensors (Basel) ; 20(20)2020 Oct 16.
Article em En | MEDLINE | ID: mdl-33081188
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 × 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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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Dióxido de Carbono / Alimentos / Análise de Alimentos Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Dióxido de Carbono / Alimentos / Análise de Alimentos Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Ano de publicação: 2020 Tipo de documento: Article