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Improvement of non-destructive detection of lamb freshness based on dual-parameter flexible temperature-impedance sensor

文献类型： 外文期刊

作者： Huang, Wentao 1 ; Xia, Jie 1 ; Wang, Xuepei 2 ; Zhao, Qinan 3 ; Zhang, Mengjie 1 ; Zhang, Xiaoshuan 1 ;

作者机构： 1.China Agr Univ, Coll Engn, Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China

2.Shanghai Jiao Tong Univ, Sch Informat & Elect Engn, Shanghai 200240, Peoples R China

3.Inner Mongolia Acad Agr & Anim Husb Sci, Hohhot 010031, Peoples R China

4.China Agr Univ, Beijing 100083, Peoples R China

关键词： Flexible multimode sensing systems; Wireless sensor networks (WSN); Impedance measurement; Freshness assessment; Machine learning

期刊名称：FOOD CONTROL （ 影响因子：6.0； 五年影响因子：5.8 ）

ISSN： 0956-7135

年卷期： 2023 年 153 卷

页码：

收录情况： SCI

摘要： Flexible sensing technology for food quality control has received some attention in recent years. The aim of this research was to develop a novel flexible sensor to assess the freshness of lamb meat, replacing the traditional rigid sensor and lamb quality indicator testing method. Firstly, a low-cost, high-performance flexible impedance-temperature electrode was prepared based on laser direct writing technology, and a complete flexible temperature-impedance measurement system was built in combination with an STM32 microcontroller. Then, a wireless sensor network (WSN) based micro-environment monitoring platform was developed to measure the quality index of lamb, collect gas information, and impedance information under three temperatures of 0 degrees C, 4 degrees C, and 8 degrees C. As the storage time of lamb increases, its biological tissue structure and intracellular ionic conductive properties change, which can be reflected by the impedance amplitude and phase angle. A machine learning prediction model is established with bio-impedance as the input and freshness as the output. The research found that the variation of lamb freshness was influenced by the storage temperature, and that the impedance mode of the flexible system was able to instantly detect the freshness of lamb with an average pre-diction accuracy of 91.63% when combined with machine learning algorithms. This is higher than the accuracy of the traditional Arrhenius equation method (83.70%) and comparable to the prediction accuracy of gas in-formation (93.24%). Additionally, the temperature mode of the flexible system was able to continuously monitor temperature changes on the surface of lamb, compensating for the inability of traditional impedance sensors to do so, and providing unique insights into accurate monitoring of lamb temperature changes. This research provides a new solution for improving food quality control during the chilled meat cold chain.

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