Microbial community profiling in meat quality and shelf-life

Abstract

The studies presented here were designed to evaluate microbial community profiles of ground beef, with the objectives of determining shelf-life and spoilage and bridging these findings to support community outreach and education initiatives. The first study aimed to identify the most effective pre-extraction sample treatment method for isolating bacterial DNA. Fresh ground beef samples weighed out to 200 mg were inoculated with the ZymoBIOMICS® Microbial Community Standard by creating a stock solution of 75 μL of the mock community and 750 μL of ZymoBIOMICS Lysis Solutions and subdivided into six potential treatment groups: two groups with direct extraction from the product (with and without the mock community), two-step centrifugation of the product and peptone buffer, swabbing the surface of the product with an SDS extraction buffer, PBS rinsate and centrifugation, and treatment with proteinase K. DNA was extracted and sequenced for shotgun metagenomics with Illumina NovaSeq X Plus. The preliminary results demonstrated Proteinase K as the most effective method for isolating DNA based on the DNA quantification. Further analysis showed two-step centrifugation as the most effective method based on metagenomic analysis. A second study objective was to identify changes in the microbial community composition over the shelf-life of beef products and associate this with key changes in meat quality and spoilage markers. A 14-day experimental period was completed to collect data through instrumental colorimeter testing, lipid oxidation, microbial aerobic plate counts, and meat compositional analysis. The microbial communities were assessed using the Illumina miSeq platform and a statistical assessment of the microbial taxonomies were used to determine changes over time. Additionally, a Random Forest regression machine learning algorithm was constructed to accurately predict the spoilage level using the shift in microbial communities. Ground beef samples weighed out to 225g, tray overwrapped, and randomly assigned pull dates and sample numbers. Samples were held in simulated retail storage in an LED-lit, 3-tier display open-curtain retail case. Quality analysis showed the samples microbially spoiled at day six of the experimental period, with further evidence through microbial analysis demonstrating an increase in Pseudomonadaceae, a common spoilage organism, during the period. There are also higher populations of Carnobacteriaceae, Listeriaceae, Streptococcaceae, Lactobacillaceae, and Yersiniaceae throughout the shelf-life, and these changes overlap with the lipid oxidation changes. The machine learning algorithm demonstrated predictable patterns of microbial communities associated with spoilage. The final study aimed to evaluate the Beef 101 program script and additional programming as an educational tool for enhancing beef production and processing knowledge, and to determine if instructor experience influenced participant learning outcomes. To evaluate and enhance this program, researchers observed previous Beef 101 programs to gain an understanding of the information provided and what is done to put the program together. Additionally, this information and relevant literature were utilized to build a script to keep the uniformity of the program. Pre- and post-program surveys were analyzed to determine the effectiveness of the material and evaluate the instructors. This data was used to determine the effectiveness of the Beef 101 program as an educational tool for enhancing beef production and processing knowledge and determine if instructor experience influenced participant learning outcomes. Observations from this study showed no significant difference between the programs given by an experienced instructor versus a student using the script. This thesis demonstrates that assessing microbial community profiles can allow for retailers to extend the shelf-life dates on packages, limiting food waste, while also demonstrating the benefits of sharing beef and meat production knowledge through community outreach efforts.