In order to establish predicting models for beef marbling grade, one hundred and thirty-five beef rib-eye samples were collected from a beef processing factory. A three-grader panel was employed to assign each sample a marbling score according to the official standard cards. Then images of each sample were captured with a CCD camera. The beef marbling images were separated and the indicators such as ratio of fat area, the total number of fat particles and numbers of the big and the small fat particles were obtained by using image processing technologies. After fractal dimensions of marbling images was calculated through the varying megascopic degree method, a multiple linear regression model and a multiple polynomial model for the prediction of marbling score were derived based on the obtained fractal dimensions and image feature parameters. Validation results showed that the correct percentage of the marbling grade predicted by the multiple linear model and by the multiple polynomial model were 75% and 87.5%, respectively, indicating that beef marbling grades can be determined by using fractal dimension and image processing method.%采集135个牛胴体眼肌样本,参照中国牛肉质量标准的大理石图版,由3人评定小组评定出每一个样本的大理石花纹等级,再用数码相机采集每一个样本的数字图像.用图像处理技术对每一幅图像进行降噪、分割等处理,提取出牛肉大理石花纹,并计算出牛肉大理石花纹的面积比率、总脂肪颗粒数和大小脂肪颗粒数；然后用变尺度的方法,测定出每个牛肉大理石花纹样本图像的计盒维数和信息维数；以这些参数为基础,分别建立牛肉大理石花纹等级判定的多元线性模型和多元多项式模型.实验结果表明,利用多元线性模型和多元多项式模型判定牛肉大理石花纹等级,预测正确率分别为75％和87.5％.
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