Degree Type

Thesis

Date of Award

2010

Degree Name

Master of Science

Department

Animal Science

First Advisor

Tom J. Baas

Abstract

Advancements in the measurement of compositional traits in live swine, genetic selection, and the lean value-based marketing system have cumulatively led to a high lean pig population. Consumer demands for leaner pork have been met industry-wide. Unfortunately, indicators of fresh pork quality and sensory attributes of cooked pork have declined as continued selection for leanness has been practiced over time. Though the healthfulness of cooked pork should remain a priority for the swine industry, pork quality issues should also be addressed. A decrease in intramuscular fat (IMF) in the loin is one of the changes in pork quality that has occurred. There has been continuous debate about relationships among pork quality traits and which of the fresh pork quality indicators is the most important determinant of cooked pork sensory attributes. The only indicator of fresh pork quality that has been successfully measured in live animals is IMF using real-time ultrasound technology.

Researchers have evaluated the accuracy of different ultrasound scanners and IMF prediction software programs for use in live beef cattle. Previous research on the prediction of IMF in swine has focused on proof of the concept and on refinement of prediction models. Prior to this study, the accuracy of different equipment and procedures for the prediction of IMF in swine has not been investigated. Therefore, the objectives of this study were to compare the accuracy of: 1) 2 commercially available ultrasound scanners, 2) 2 image capturing devices, 3)2 image collection methods, and 4) 3 region of interest box options.

Four hundred fifty-four barrows and gilts were scanned, harvested at a commercial packing plant, and chemical IMF was determined from a loin sample. Predicted IMF was determined for the different combinations of equipment and procedures and compared with chemical IMF to evaluate accuracy. Bias, standard error of prediction (SEP), and the absolute difference (ABSDiff) between predicted and chemical IMF were used to compare accuracy.

The Aloka 500 and Aquila Vet provide similar accuracy when compared using SEP. The Aloka 500 was more accurate when ABSDiff was analyzed with a linear model. Use of Sensoray images gave more accurate predictions of IMF than VCE images. Further enhancements to the live video image collection process are needed before the concept can be viable for commercial use. The use of multiple region of interest boxes on images improves IMF prediction accuracy.

DOI

https://doi.org/10.31274/etd-180810-1678

Copyright Owner

Kyle Joseph Schulte

Language

en

Date Available

2012-04-30

File Format

application/pdf

File Size

100 pages

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