食品科学

• 基础研究 • 上一篇    下一篇

基于决策树雪花牛肉大理石花纹分级模型

梁 琨1,2,丁 冬1,彭增起3,沈明霞1,*,林盛业1,曹 辉4   

  1. 1.南京农业大学工学院,江苏 南京 210031;2.江苏省现代设施农业技术与装备工程实验室,江苏 南京 210031;
    3.南京农业大学食品科技学院,江苏 南京 210095;4.陕西秦宝牧业股份有限公司,陕西 宝鸡 721000
  • 出版日期:2015-09-15 发布日期:2015-09-11

Classification of Snowflake Beef Marbling Grades Based on Decision Tree

LIANG Kun1,2, DING Dong1, PENG Zengqi3, SHEN Mingxia1,*, LIN Shengye1, CAO Hui4   

  1. 1. College of Engineering, Nanjing Agricultural University, Nanjing 210031, China;
    2. Jiangsu Province Engineering Laboratory for Modern Facility Agriculture Technology and Equipment, Nanjing 210031, China;
    3. College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China;
    4. Limited Liability Company of Qin Bao Animal Husbandry in Shaanxi Province, Baoji 721000, China
  • Online:2015-09-15 Published:2015-09-11

摘要:

为建立雪花牛肉大理石花纹等级评价方法,根据不同等级雪花牛肉大理石花纹图像特征及人工评级的标准,确定了影响大理石花纹的等级主要因素。本研究提出影响大理石花纹等级的几何参数特征、几何分布参数特征和统计参数特征。其中几何参数特征主要反映大理石花纹面积、周长等;几何分布特征主要反映大理石花纹图像中脂肪颗粒沉积的密度,根据脂肪颗粒沉积情况可分为大颗粒脂肪、中颗粒脂肪、小颗粒脂肪等;统计参数特征主要反映大理石花纹丰富程度以及大理石花纹分布均匀性。利用相关性分析提取影响雪花牛肉大理石花纹等级的特征参数。建立基于C4.5和CART算法的决策树模型,结果表明:对于C4.5算法建立的决策树分级模型,三级和五级大理石花纹分级预测精度分别为91.80%、92.31%,而该模型针对四级样本建立的模型无效,其结果多数误判为三级;对于CART算法建立的决策树模型同样存在这样的问题,即三级和五级大理石花纹分级预测精度高,而对四级样本分级无效。

关键词: 雪花牛肉, 大理石花纹, 分级模型, 决策树

Abstract:

In order to establish a method to evaluate snowflakes beef marbling grades, the main factors affecting grading
marbling were identified by comparing the image features with artificial rating criteria of different snowflakes beef
marbling grades. This study presented the geometric feature parameters, geometric distribution feature parameters and
statistical feature parameters affecting marbling grade. The geometric feature parameters mainly reflected the marbling
area, perimeter and so on. The geometric distribution feature parameters mainly reflected the different deposition densities of
large, medium and small fat particles in the marbling image. The statistical feature parameters mainly reflected the marbling
abundance and marbling distribution uniformity. Correlation analysis between the features parameters extracted and
snowflake beef marbling grades was conducted. Decision tree models were established based on C4.5 and CART algorithm,
and the results showed that the prediction accuracy of three-level and five-level grades were 91.80% and 92.31%, respectively,
however, the model for the four-level sample model was invalid and the misjudgment results were mostly three-level. The
same problem existed in the prediction accuracy of models based on CART algorithm.

Key words: snowflake beef, marbling, classification model, decision tree

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