Multivariate regression analysis applied to the calibration of equipment used in pig meat classification in Romania

Meat Sci. 2016 Jun;116:16-25. doi: 10.1016/j.meatsci.2016.01.011. Epub 2016 Jan 26.

Abstract

This paper highlights the statistical methodology used in a dissection experiment carried out in Romania to calibrate and standardize two classification devices, OptiGrade PRO (OGP) and Fat-o-Meat'er (FOM). One hundred forty-five carcasses were measured using the two probes and dissected according to the European reference method. To derive prediction formulas for each device, multiple linear regression analysis was performed on the relationship between the reference lean meat percentage and the back fat and muscle thicknesses, using the ordinary least squares technique. The root mean squared error of prediction calculated using the leave-one-out cross validation met European Commission (EC) requirements. The application of the new prediction equations reduced the gap between the lean meat percentage measured with the OGP and FOM from 2.43% (average for the period Q3/2006-Q2/2008) to 0.10% (average for the period Q3/2008-Q4/2014), providing the basis for a fair payment system for the pig producers.

Keywords: Calibration; Carcass classification; Pig meat; Regression analysis; Standardization.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adipose Tissue / anatomy & histology*
  • Animals
  • Body Composition
  • Calibration
  • Food Handling / instrumentation*
  • Meat / classification*
  • Romania
  • Swine