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                            Library & Multimedia
 The Microbiome Surrounding Death and Decay: Microbial Ecology of Food Processing, Meat Spoilage, and Human Decomposition Environments
NCJ Number
              306481

Author(s)
          Aeriel D. Belk
Date Published

2021
Length
              181 pages
Annotation
              This dissertation presents the results of a series of studies which explored the microbial community development and succession in distinct environments that have the potential to influence the decomposition patterns of vertebrate remains, with implications for meat spoilage resulting in food waste, as well as human decomposition which impacts forensic examinations and the determination of postmortem interval.
Abstract
              The author of this dissertation demonstrates real-world applications of microbial ecology that can protect human health and well-being, as well as potentially help solve crimes through forensic analysis. The dissertation presents the author’s exploration of questions related to the vertebrate decomposition environment to several systems: the built environment, meat spoilage, and human decomposition for forensic sciences. The overarching goal was to demonstrate the patterns with which microbial communities assemble and progress in those specific environments, and to show the applications of that knowledge to the larger industry and research fields. The research projects analyzed in this dissertation specifically investigated the following: the microbiome of the built environment of a meat processing facility, in order to investigate knowledge gaps surrounding how microbial communities initially form in a food processing environment; the microbial communities associated with chicken breasts that were chilled using air or water, leading to different patterns of spoilage over time; and patterns in microbial succession in decomposition environments to determine how decomposition changes the microbial ecology and methods by which those patterns can be used to predict postmortem interval (PMI), as well as how to build the most robust Random Forest regression models for prediction of PMI by testing models built on different sample types, gene markers, and taxonomic levels. The author also sought to improve the understanding of microbial communities in postmortem human environments by evaluating the patterns of microbial succession associated with human remains at three geographically distinct locations.
      
  
          Additional Details
Grant Number(s)
        2018-R2-CX-0017


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Thesis/Dissertation


  
  
        
  Award
        Improving Machine Learning Methods for Predicting the Postmortem Interval Using Microbiome Data
    
  
  
Date Published: January 1, 2021
    
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