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Quantitative Analysis of Fruit Internal Physical and Chemical Indicators Based on Magnetic Induction Tomography

文献类型： 外文期刊

作者： Chen, Zuohui 1 ; Chen, Cheng 3 ; Lyu, Weihao 1 ; Cai, Chang 1 ; Xu, Ning 4 ; Zhu, Junwei 1 ; Cheng, Yuan 5 ; Chen, Cheng 3 ; Xiang, Yun 1 ;

作者机构： 1.Zhejiang Univ Technol ZJUT, Inst Cyberspace Secur, Hangzhou, Peoples R China

2.ZJUT, Binjiang Inst Artificial Intelligence, Hangzhou 310056, Peoples R China

3.Zhejiang Yuncheng Informat Technol Co Ltd, Hangzhou 310023, Peoples R China

4.Zhejiang Univ Technol, Inst Drug Dev & Chem Biol, Hangzhou 313200, Zhejiang, Peoples R China

5.Zhejiang Acad Agr Sci, Inst Vegetables, Hangzhou 310021, Peoples R China

6.Hangzhou Yongchuan Technol Co Ltd, Hangzhou 310023, Peoples R China

关键词： Coils; Conductivity; Magnetic field measurement; Accuracy; Hyperspectral imaging; Signal to noise ratio; Solids; Magnetic resonance imaging; Frequency division multiplexing; Conductivity measurement; Magnetic induction tomography; nondestructive; physical and chemical indicators (PCIs)

期刊名称：IEEE SENSORS JOURNAL （ 影响因子：4.5； 五年影响因子：4.7 ）

ISSN： 1530-437X

年卷期： 2025 年 25 卷 5 期

页码：

收录情况： SCI

摘要： Nondestructive measurement of physical and chemical indicators (PCIs) in fruits and vegetables is essential for quality control in agriculture. However, existing techniques such as hyperspectral and near-infrared (NIR) spectroscopy face limitations in terms of high costs, noise sensitivity, low efficiency, and reduced accuracy under real-world conditions. In this work, we propose a novel approach using magnetic induction tomography (MIT) to address these issues, offering enhanced accuracy, noise resistance, and cost-effectiveness. Specifically, we design and implement a PCIs measurement system based on MIT, which is previously unexplored in this article. In addition, we develop an efficient, portable system with customized regression models that map MIT conductivity data to quantitative PCI values, enabling practical field applications. Both controlled and real-world experiments show that our MIT system achieves an accuracy of 97% and 81% in predicting the freshness of tomatoes and grapes, respectively, and improves the ${R}<^>{{2}}$ value in tomato acidity prediction by 32.9% over NIR methods, demonstrating its effectiveness for nondestructive agricultural quality assessments.

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