6533b826fe1ef96bd1283d7e
RESEARCH PRODUCT
Multivariate regression analysis applied to the calibration of equipment used in pig meat classification in Romania.
Marian LabaRoxana Florenta Savescusubject
Multivariate statisticsMeatMean squared errorFood HandlingSwine0211 other engineering and technologies02 engineering and technologyCross-validationStatisticsCalibrationMedicineAnimals021110 strategic defence & security studiesbusiness.industryBack fatRomania0402 animal and dairy scienceRegression analysis04 agricultural and veterinary sciences040201 dairy & animal scienceAdipose TissueOrdinary least squaresCalibrationBody CompositionMultiple linear regression analysisbusinessFood Sciencedescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-06-01 | Meat science |