Wi-freshness：基于CSI的猪肉新鲜度检测系统研究

doi:10.11959/j.issn.2096-3750.2023.00332

物联网学报 ›› 2023, Vol. 7 ›› Issue (2): 143-152.doi: 10.11959/j.issn.2096-3750.2023.00332

• 理论与技术 • 上一篇

Wi-freshness：基于CSI的猪肉新鲜度检测系统研究

牛超¹^,²^,³, 杨卫东¹^,²^,³, 胡鹏明²^,³, 高向上¹^,²^,³, 沈二波²^,³

¹ 河南工业大学信息科学与工程学院，河南郑州 450001
² 河南省粮食光电探测与控制重点实验室（河南工业大学），河南郑州 450001
³ 粮食信息处理与控制教育部重点实验室（河南工业大学），河南郑州 450001

修回日期:2023-03-10 出版日期:2023-06-30 发布日期:2023-06-01
作者简介:牛超（1995- ），男，河南工业大学信息科学与工程学院硕士生，主要研究方向为物联网、无线感知、视频安全等
杨卫东（1977- ），男，博士，河南工业大学教授，主要研究方向为无线网络安全、物联网、隐私保护、车载自组织网络
胡鹏明（1994- ），男，河南省粮食光电探测与控制重点实验室（河南工业大学）博士生，主要研究方向为物联网、无线感知
高向上（1997- ），男，河南工业大学信息科学与工程学院硕士生，主要研究方向无线感知、人工智能
沈二波（1984- ），男，河南省粮食光电探测与控制重点实验室（河南工业大学）博士生，主要研究方向无线感知、人工智能
基金资助:
国家自然科学基金资助项目(62172141);国家自然科学基金资助项目(61772173);河南省自然科学基金资助项目(222300420004);河南省重大公益专项(201300210100);河南省留学人员科研择优资助项目(2124003)

Wi-freshness: research on CSI-based pork freshness detecting system

Chao NIU¹^,²^,³, Weidong YANG¹^,²^,³, Pengming HU²^,³, Xiangshang GAO¹^,²^,³, Erbo SHEN²^,³

¹ College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
² Henan Province Key Laboratory of Grain Photoelectric Detection and Control(Henan University of Technology), Zhengzhou 450001, China
³ Key Laboratory of Grain Information Processing and Control(Henan University of Technology), Ministry of Education, Zhengzhou 450001, China

Revised:2023-03-10 Online:2023-06-30 Published:2023-06-01
Supported by:
The National Natural Science Foundation of China(62172141);The National Natural Science Foundation of China(61772173);The Natural Science Foundation of Henan Province(222300420004);The Major Public Welfare Project of Henan Province(201300210100);The Scientific Research Program for Overseas Students of Henan Province(2124003)

摘要/Abstract

摘要：

有效、快速地评价猪肉新鲜度对猪肉品质监测具有重要意义。然而，传统的感官评价方法过于主观，理化分析又耗时过长且具有破坏性。虽然射频信号被用于定位、材料识别、生命体征监测，但在肉类新鲜度检测方面并未受到重视。提出了一种实时、无损、低成本的基于信道状态信息（CSI, channel state information）的猪肉新鲜度检测系统Wi-freshness。该系统基于泛在网络（商用Wi-Fi）部署和实施，是物联网在生鲜农产品领域一个新的应用。Wi-freshness包括CSI数据感知、数据预处理、检测建模和新鲜度检测4个模块。考虑Wi-freshness需要处理的数据特征值不多，以及对预测的实时性要求较高的特点，提出一种基于宽度学习系统（BLS, broad learning system）的检测模型。实验结果表明，Wi-freshness系统能达到93%以上的检测准确率。

关键词: 信道状态信息, Wi-Fi, 猪肉新鲜度检测, 宽度学习系统

Abstract:

Effective and rapid detection of pork freshness is important for pork quality.However, traditional sensory evaluation methods are too subjective, physical and chemical analysis methods are time-consuming and destructive.Recently, radio frequency is widely used in the field of location, material identification and human body monitoring, while, the meat freshness detection is ignored.A real-time, non-destructive and low-cost system for pork freshness detecting based on channel state information (CSI) was proposed.It is a new application of internet of things in the field of fresh agricultural products based on ubiquitous network (commercial Wi-Fi).The proposed Wi-freshness consists of four modules: CSI data sensing, data pre-processing, detection modelling and freshness detection.Considering the need for Wi-freshness data characteristic value processing is not much, and high demand for real-time prediction characteristics, a detection model based broad learning system (BLS) was proposed.Experiment shows that Wi-freshness system can achieve more than 93% detection accuracy.

Key words: channel state information, Wi-Fi, pork freshness detection, broad learning system

中图分类号:

TP39

牛超, 杨卫东, 胡鹏明, 高向上, 沈二波. Wi-freshness：基于CSI的猪肉新鲜度检测系统研究[J]. 物联网学报, 2023, 7(2): 143-152.

Chao NIU, Weidong YANG, Pengming HU, Xiangshang GAO, Erbo SHEN. Wi-freshness: research on CSI-based pork freshness detecting system[J]. Chinese Journal on Internet of Things, 2023, 7(2): 143-152.

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参考文献 30

[1]	LI S Y , CHEN S J , ZHUO B G ,et al. Flexible ammonia sensor based on PEDOT:PSS/silver nanowire composite film for meat freshness monitoring[J]. IEEE Electron Device Letters, 2017,38(7): 975-978.
[2]	LU S C , WANG X F , ROCHA á , . Modeling the fuzzy cold storage problem and its solution by a discrete firefly algorithm[J]. Journal of Intelligent ＆ Fuzzy Systems:Applications in Engineering and Technology, 2016,31(4): 2431-2440.
[3]	ALI I , NAGALINGAM S , GURD B . A resilience model for cold chain logistics of perishable products[J]. The International Journal of Logistics Management, 2018,29(3): 922-941.
[4]	LIU R , XING L J , ZHOU G H ,et al. What is meat in China?[J]. Animal Frontiers, 2017,7(4): 53-56.
[5]	XIONG L , HU Y , LIU C ,et al. Detection of total volatile basic nitrogen (TVB-N) in pork using Fourier transform near-infrared (FT-NIR) spectroscopy and cluster analysis for quality assurance[J]. Transactions of the ASABE, 2012,55(6): 2245-2250.
[6]	WENG X H , LUAN X Y , KONG C ,et al. A comprehensive method for assessing meat freshness using fusing electronic nose,computer vision,and artificial tactile technologies[J]. Journal of Sensors, 2020: 1-14.
[7]	CHANG H Y , LI H , HU Y J . An intelligent method of detecting pork freshness based on digital image processing[C]// Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things. Piscataway:IEEE Press, 2015: 107-110.
[8]	GRASSI S , BENEDETTI S , OPIZZIO M ,et al. Meat and fish freshness assessment by a portable and simplified electronic nose system (mastersense)[J]. Sensors (Basel,Switzerland), 2019,19(14): 3225.
[9]	HAN F K , HUANG X Y , TEYE E ,et al. Nondestructive detection of fish freshness during its preservation by combining electronic nose and electronic tongue techniques in conjunction with chemometric analysis[J]. Analytical Methods, 2014,6(2): 529-536.
[10]	TAHERI-GARAVAND A , FATAHI S , SHAHBAZI F ,et al. A nondestructive intelligent approach to real-time evaluation of chicken meat freshness based on computer vision technique[J]. Journal of Food Process Engineering, 2019,42(4): e13039.
[11]	SUN X , YOUNG J , LIU J H ,et al. Prediction of pork loin quality using online computer vision system and artificial intelligence model[EB]. 2018.
[12]	ZOU L , LIU W N , LEI M ,et al. An improved residual network for pork freshness detection using near-infrared spectroscopy[J]. Entropy (Basel,Switzerland), 2021,23(10): 1293.
[13]	CRICHTON S O J , KIRCHNER S M , PORLEY V ,et al. Classification of organic beef freshness using VNIR hyperspectral imaging[J]. Meat Science, 2017(129): 20-27.
[14]	KAUSHIK S . An overview of technical aspect for Wi-Fi networks technology[J]. International Journal of Electronics and Computer Science Engineering, 2012,1(1): 28-34.
[15]	LASORTE N , BARNES W J , REFAI H H . The history of orthogonal frequency division multiplexing[C]// Proceedings of IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference. Piscataway:IEEE Press, 2008: 1-5.
[16]	WANG H , ZHANG D Q , WANG Y S ,et al. RT-fall:a real-time and contactless fall detection system with commodity Wi-Fi devices[J]. IEEE Transactions on Mobile Computing, 2017,16(2): 511-526.
[17]	WANG Y , LIU J , CHEN Y Y ,et al. E-eyes:device-free location-oriented activity identification using fine-grained Wi-Fi signatures[C]// Proceedings of the 20th Annual International Conference on Mobile Computing and Networking. New York:ACM Press, 2014: 617-628.
[18]	WANG X Y , GAO L J , MAO S W ,et al. CSI-based fingerprinting for indoor localization:a deep learning approach[J]. IEEE Transactions on Vehicular Technology, 2017,66(1): 763-776.
[19]	HU P , YANG W , WANG X ,et al. Contact-free wheat mildew detection with commodity Wi-Fi[J]. International Journal of Cognitive Computing in Engineering, 2022,3: 9-23.
[20]	MUTWAKI L . Meat spoilage mechanisms and preservation techniques:a critical review[J]. American Journal of Agricultural and Biological Sciences, 2011,6(4): 486-510.
[21]	MRLEIN D . Sensory evaluation of meat and meat products:fundamentals and applications[J]. IOP Conference Series:Earth and Environmental Science, 2019,333(1): 012007.
[22]	DAS A K , NANDA P K , DAS A ,et al. Hazards and safety issues of meat and meat products[M]// Food Safety and Human Health. Amsterdam: Elsevier, 2019: 145-168.
[23]	KOMAROV V , WANG S , TANG J . Permittivity and measurements[M]// Encyclopedia of RF and Microwave Engineering. Hoboken: John Wiley ＆ Sons,Inc., 2005: 36-46.
[24]	BEKHIT E , HOLMAN B , GITERU S G ,et al. Total volatile basic nitrogen (TVB-N) and its role in meat spoilage:a review[J]. Trends in Food Science ＆ Technology, 2021(109): 280-302.
[25]	WU C S , YANG Z , ZHOU Z M ,et al. PhaseU:real-time LOS identification with Wi-Fi[C]// Proceedings of 2015 IEEE Conference on Computer Communications (INFOCOM). Piscataway:IEEE Press, 2015: 2038-2046.
[26]	NEE R V , PRASAD R . OFDM for wireless multimedia communications[EB]. 1999.
[27]	HALPERIN D , HU W J , SHETH A ,et al. 802.11 with multiple antennas for dummies[J]. ACM SIGCOMM Computer Communication Review, 2010,40(1): 19-25.
[28]	SEN S , RADUNOVIC B , CHOUDHURY R R ,et al. You are facing the Mona Lisa:spot localization using PHY layer information[C]// Proceedings of the 10th International Conference on Mobile Systems,Applications,and Services. New York:ACM Press, 2012: 183-196.
[29]	FENG C , XIONG J , CHANG L Q ,et al. WiMi:target material identification with commodity Wi-Fi devices[C]// Proceedings of 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). Piscataway:IEEE Press, 2019: 700-710.
[30]	CHEN C L P , LIU Z L . Broad learning system:an effective and efficient incremental learning system without the need for deep architecture[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018,29(1): 10-24.

数据分类算法	准确率	训练时间/s
RF	91.5%	371
SVM	91.6%	267
CNN	92.8%	382
BLS	93%	198

Wi-freshness：基于CSI的猪肉新鲜度检测系统研究

Wi-freshness: research on CSI-based pork freshness detecting system

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