基于高光谱图像纹理特征的牛肉嫩度分布评价  

Distribution of beef tenderness grading based on texture features by hyperspectral image analysis

作　　者：赵娟[1] 彭彦昆[1] 

机构地区：[1]中国农业大学工学院,国家农产品加工技术装备研发分中心,北京100083

出　　处：《农业工程学报》2015年第7期279-286,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：公益性行业(农业)科研专项经费资助项目(201003008);国家科技支撑计划项目(2012BAH04B00)

摘　　要：传统牛肉品质的检测方法耗时长,效率低,破坏样品,已不能满足现代化生产的需要。为了实现对牛肉嫩度品质的快速无损检测和评价,该文利用高光谱成像系统,以西门塔尔牛多个胴体的背最长肌部位为研究对象,采集56个有效样本的高光谱立体图像,研究无损评价牛肉样品的嫩度分布。通过提取样本的反射光谱信息,并利用逐步回归算法结合遗传算法(GA,genetic algorithm)筛选出牛肉剪切力值(WBSF,warner-bratzler shear force)的特征波段。利用主成分分析(PCA,principle component analysis)提取样品的3个主成分。基于选出的特征波段图像和提取的主成分,通过计算图像灰度共生矩阵求取每幅图像8个主要纹理特征参数,分别建立了基于支持向量机(SVM,support vector machine)和线性判别(LDA,linear discriminant analysis)法的嫩度等级判别模型。经分析比较,基于主成分纹理特征优于基于特征波段图像建立的预测模型,并且,线性判别模型识别准确率相比支持向量机模型较高。基于主成分纹理特征建立的线性判别模型预测集判别精度为94.44%。研究结果证明,基于高光谱图像纹理特征分析,可以建立牛肉的嫩度判别模型,对牛肉嫩度快速无损检测技术研究提供理论参考。Due to high protein, low fat, high vitamin and mineral content, beef is regarded as an important meat item which is consumed by human in regular basis. In addition, with the improvement of people＇s living standard, different quality parameters such as beef tenderness, water retention and other edible quality indicators have become important factors to consumers＇ satisfaction. Sensory evaluation and shear force measurement are primarily two methods for measurement of beef tenderness, which are now considered as traditional methods. However, with improvement in technology, optical methods are playing important role for rapid, real-time, non-destructive and accurate detection of meat quality parameters. Near-infrared method and hyperspectral imaging method are important methods for non-destructive measurement of beef tenderness. This study focused on the application of hyperspectral image analysis for non-destructive detection of beef tenderness at different points of the sample. Considering the measurement complexity due to complex beef structure as well as the disadvantage of near infrared spectrurn for single point detection, this study made use of hyperspectral imaging system to obtain three-dimensional information from each sample. Fifty-six beef samples were obtained from the slaughter house Yuxiangyuan in Beijing and were cut into 3 cm thick slices, vacuum packaged and then stored in 4~C environment before hyperspectral image acquisition. Reflectance spectrum was obtained for each sample from acquired images. Stepwise regression and genetic algorithm （GA） were used to identify the feature wavelengths, thus reducing the high dimensionality of the hyperspectral data. Eight feature wavelengths were selected, which were used to develop prediction model for measuring the shear force value at each point in the sample. By the principal component analysis （PCA）, every sample could acquire 5 PCA images. Gray level co-occurrence matrix （GLCM） was applied to extract 8 main texture parameters from the cha

关 键 词：肉 模型 图像处理 高光谱图像 牛肉嫩度 特征波段 主成分分析 纹理特征 

分 类 号：S123[农业科学—农业基础科学]