Thesis

Applications of multivariate statistics in honey bee research, analysis of metabolomics data from samples of honey bee propolis

Creator
Rights statement
Awarding institution
  • University of Strathclyde
Date of award
  • 2020
Thesis identifier
  • T15538
Person Identifier (Local)
  • 201679467
Qualification Level
Qualification Name
Department, School or Faculty
Abstract
  • Honey bees play a significant role both ecologically and economically, through the pollination of flowering plants and crops. Additionally, honey is an ancient food source that is highly valued by different religions and cultures and has been shown to possess a wide range of beneficial uses, including cosmetic treatment, eye disease, bronchial asthma and hiccups. In addition to honey, honey bees also produce beeswax, pollen, royal jelly and propolis. In this thesis, data is studied which comes from samples of propolis from various geographical locations. Propolis is a resinous product, which consists of a combination of beeswax, saliva and resins that have been gathered by honey bees from the exudates of various surrounding plants. It is used by the bees to seal small gaps and maintain the hives, but is also an anti-microbial substance that may protect them against disease. The appearance and consistency of propolis changes depending on the temperature; it becomes elastic and sticky when warm, but hard and brittle when cold. Furthermore, its composition and colour varies from yellowish-green to dark brown, depending on its age and the sources of resin from the environment. Propolis is a highly biochemically active substance with many potential benefits in health care, which have attracted much attention. Biochemical analysis of propolis leads to highly multivariate metabolomics data. The main benefit of metabolomics is to generate a spectrum, in which peaks correspond to different chemical components, making possible the detection of multiple substances simultaneously. Relevant spectral features may be used for pattern recognition. The purpose of this research is to study methods used for statistical analysis of biochemical data arising from propolis samples. We investigate the use of different statistical methods for metabolomics data from chemical analysis of propolis samples using Mass Spectrometry (MS). Methods studied will include pre-treatment methods and multivariate analysis techniques including principal component analysis (PCA), multidimensional scaling (MDS), and clustering methods including hierarchical cluster analysis (HCA), k-means clustering and self organising maps (SOMs). Background material and results of data analysis will be presented from samples of propolis from beehives in Scotland, Libya and Europe. Conclusions are drawn in terms of the data sets themselves as well as the properties of the different methods studied for analysing such metabolomics data.
Advisor / supervisor
  • Gray, Alison
Resource Type
Note
  • This thesis was previously held under moratorium from 20/04/2020 to 20/04/2022
DOI
Date Created
  • 2020
Former identifier
  • 9912881692902996

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