食品科学 ›› 2010, Vol. 31 ›› Issue (23): 84-87.doi: 10.7506/spkx1002-6630-201023020

• 基础研究 • 上一篇    下一篇

基于猪胴体影像分级仪的我国商品猪瘦肉率预测方程的建立

尹 佳,周光宏* ,徐幸莲   

  1. 南京农业大学 国家肉品质量安全控制工程技术研究中心
  • 收稿日期:2010-05-13 修回日期:2010-11-26 出版日期:2010-12-15 发布日期:2010-12-29
  • 通讯作者: 周光宏 E-mail:ghzhou@njau.edu.cn
  • 基金资助:

    国家公益性行业科研专项 (200903012)

CSB Image Meater Use based Predictive Modeling Lean Meat Percentage of Commercial Pig Carcasses in China

YIN Jia,ZHOU Guang-hong*,XU Xing-lian   

  1. National Center of Meat Quality and Safety Control, Nanjing Agricultural University, Nanjing 210095, China
  • Received:2010-05-13 Revised:2010-11-26 Online:2010-12-15 Published:2010-12-29
  • Contact: ZHOU Guang-hong E-mail:ghzhou@njau.edu.cn

摘要:

为了能够准确预测我国商品猪的胴体瘦肉率,完成生猪的在线快速分级、快速结算,最终实现优质优价,研究运用CSB-Image-Meater 猪智能化影像分级仪,挑选436 头不同类型的商品猪测定其瘦肉率、热胴体质量、背膘厚度和肌肉厚度等指标。通过多元线性逐步回归建立基于CSB-Image-Meater 的预测商品猪瘦肉率的回归方程。结果表明:瘦肉率预测方程y=61.264 - 0.583xl +0.173x2(xl 为猪胴体臀中肌处的最薄膘厚度(F 值),x2 为臀中肌末端到脊髓管边缘处垂直距离(R 值),校正决定系数R2 为0.87,残差标准差RSD 为2.31%),作为CSB-Image-Meater 猪智能化影像分级仪的瘦肉率测定模型,拟合程度较好。方程预测值与实际值差异不显著,方程预测准确率较高,效果良好,完全可以应用于实际生产中。

关键词: Image-Meater, 猪胴体, 瘦肉率, 预测方程

Abstract:

To accurately predict lean meat percentage of commercial pig carcasses in China and to achieve grading on line and fast accounting so as to optimize price and quality, CSB image meater was employed to analyze lean meat percentages, hot carcass weights, back-fat thicknesses and muscle thicknesses of 436 different types of commercial pig carcasses. A regression equation for predicting lean meat percentage of commercial pig carcasses was established based on the data from CSB image meater through multiple linear regression as follows: y = 61.264-0.583x1 + 0.173x2, where x1 was the thickness of the thinnest back fat, and x2 was the vertical distance between the end of gluteus medius muscle and the edge of spinal column, with a determination coefficient R2 of 0.87 and a standard residual error of 2.31%, indicating good degree of fitness. No significant difference between the actual and model-predicted values was observed. Therefore, the established equation has a high accuracy and is suitable to be applied in practice.

Key words: image meater, pig carcass, lean meat percentage, prediction

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