食品科学

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基于多源感知信息融合的牛肉新鲜度分级检测

姜沛宏,张玉华,陈东杰,张长峰,郭风军   

  1. 山东商业职业技术学院,山东省农产品贮运保鲜技术重点实验室,山东国家农产品现代物流工程技术研究中心,山东 济南 250103
  • 出版日期:2016-03-25 发布日期:2016-03-18

Measurement of Beef Freshness Grading Based on Multi-Sensor Information Fusion Technology

JIANG Peihong, ZHANG Yuhua*, CHEN Dongjie, ZHANG Changfeng, GUO Fengjun   

  1. Shandong National Engineering Research Center for Agricultural Products Logistics, Shandong Key Laboratory of Storage and Transportation Technology of Agricultural Products, Shandong Institute of Commerce and Technology, Jinan 250103, China
  • Online:2016-03-25 Published:2016-03-18

摘要:

利用机器视觉和近红外光谱的多源感知信息融合技术评判牛肉新鲜度,并开发了相关的识别系统。以牛后腿肉为研究对象,对获取的图像特征信息和光谱特征信息,采用BP神经网络建立牛肉新鲜度分级模型。其中,通过主成分分析提取相应的主成分因子作为建模输入,根据挥发性盐基氮含量划分新鲜度等级作为模型输出。结果发现,在图像特征信息因子数为6、光谱信息主成分因子数为6时,建立的模型预测识别率可达98.31%。结果表明,基于机器视觉和近红外光谱技术的多源感知信息融合技术评判牛肉新鲜度的方法可行。

关键词: 信息融合, 牛肉新鲜度, 无损检测, 机器视觉, 近红外光谱

Abstract:

This study tested a new idea that beef freshness could be discriminated with multi-sensor information fusion from
machine vision and near infrared spectroscopy, meanwhile, successfully developed a system one can do this work on. In this
experiment, rump steak was used as experimental target. Based on feature variables from image information and spectral
information, back propagation (BP) neural network was adopted to establish a beef freshness classification model. Principal
component analysis (PCA) was implemented and the principal components (PCs) were extracted as the inputs, the freshness
divided by total volatile base nitrogen (TVB-N) contents as the outputs. Experimental results showed that the discrimination
rate equaled 98.31% under PCs = 6 for both image information and spectral information. The overall results showed that it
was feasible to discriminate beef freshness with two-sensor information fusion.

Key words: multi-sensor information fusion, beef freshness, nondestructive testing, machine vision, near infrared spectroscopy

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