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

• Romanian dissection trial for calibration of probes used in pig classification was evaluated.
• Multivariate regression was used to develop the prediction models.
• The predictive models used for calibration meet statistical tests requirements.
• There was no need to allow for gender effects in the prediction of the lean meat.
• The models were adopted in national legislation after EU requirements were fulfilled.

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.