Capabilities of rapid evaporative ionization mass spectrometry to predict lamb flavor and overview of feeding genetically modified grain to livestock
Date
2019
Authors
Gifford, Cody Lynn, author
Woerner, Dale, advisor
Belk, Keith, committee member
Engle, Terry, committee member
Prenni, Jessica, committee member
Heuberger, Adam, committee member
Journal Title
Journal ISSN
Volume Title
Abstract
The objective of experiment 1 was to evaluate the ability of rapid evaporative ionization mass spectrometry (REIMS) to predict characteristics of cooked sheep meat flavor using metabolomic data from raw samples. Boneless leg samples were obtained from 150 carcasses of sheep representing three age classifications (n=50 per age classification), at three USDA inspected harvest facilities located in Colorado and California, between October 2017 to June 2018. A trained descriptive panel rated seven flavor attributes. Metabolomic data from fat, lean and ground patties from legs of sheep carcasses were captured through the REIMS platform. Principal component analysis factor scores were used in hierarchical cluster analysis to assess two-level and three-level sensory clusters. Partial least squares (PLS) was used to reduce dimensionality of data before the linear discriminant analysis (LDA) model was built. Eighty percent of the samples were randomly selected to train models and the remaining 20% were used to test prediction accuracy. Mutton carcasses were identified with 88.9% sensitivity and 80.0% precision using external fat of the leg and with 100% sensitivity and 90.9% precision using ground patties. Yearling carcasses were identified with 85.7% precision using lean and lambs were predicted with 70% precision using lean and fat tissue. Greater than 80% accuracy (overall and balanced), sensitivity and precision was achieved in models using lean and ground patties to identify production background (whether the live animal that produced the lean or ground patties was grain-finished or grass-finished). Prediction accuracies of age classification, production background and two-level flavor performance categories were 68% or higher with various machine learning algorithms coupled with data dimension reduction approaches. Further work is warranted to validate use of this technology in an on-line production setting and additional datasets could be used to further refine or create additional prediction models with better understanding of data processing characteristics. The review was conducted to assess the scientific literature for evidence of altered health effects in livestock species that have been fed genetically modified grain and any health effects discussed in reference to human consumption of meat products from those animals. Public concern still exists for feeding genetically modified (GM) or genetically engineered (GE) corn to animals that produce animal protein foods. In the U.S., 90% of all corn acres planted in 2013 were from single herbicide or insect resistance GE corn varieties. Regulation of GE crops is mandatory in the U.S. and consists of review and approval by three different Federal agencies. Substantial equivalence is a principle used in evaluating the safety of GE crops to establish that transgenic (GE or GM) varieties are nutritionally similar and as safe as non-transgenic crops. Animal feeding trials can provide further information to establish the safety of GE crops for human and animal consumption. No publications were found that had reported human metabolic effects from consuming beef cattle fed genetically modified grains. No consistent conclusions have been made that feeding GE corn to mice or rats, beef or dairy cattle, swine, or poultry causes any adverse effects to health. Parameters regarding sample size, diet treatments and specified controls exist to guide researchers in designing animal feeding trials with GE crops, but many criticisms of the scientific literature still exist. Additionally, published feeding trials conducted with transgenic corn grain and silage in beef cattle are limited.