Date of Submission

12-20-2022

Document Type

Thesis

Department

Forensic Science

Advisor

Heather Miller Coyle, Ph.D.

Keywords

Forensic Science, Forensic Palynology, Pollen Counts, Plant Species Identification, Crime Scene Investigation, Seasonal Pollen

LCSH

Palynology, Criminal investigation, Forensic sciences, Plants--Identification

Abstract

Continuous advancement of palynology-based scientific techniques and development of novel forensic tools have the potential to improve investigative approaches and outcomes in forensic criminal investigations. One such forensic tool has the potential to create new leads in criminal cases through analysis and comparison of pollen assemblages, taken from either individuals or locations of question. Investigators can use this technique in criminal investigations to narrow down suspect polls by elimination or confirmation of evidence. Despite the potential pollen analysis holds for forensic investigations and crime scene reconstruction, this field of study is underdeveloped compared to other approaches. Due to the lack of available training for forensic palynologists and limited funding, the amount of information surrounding this technique is not enough to consistently prove its worth in court. The purpose of this study is to determine optimal locations for pollen sample collection, constrained to the face and hands, which yields the greatest number of pollen grains. This is achieved by counting the pollen grain accumulations on four locations on the face and two locations on the hands. Seven subjects were swabbed at six locations at which pollen grains were counted with the aid of a compound light microscope and visualized using Dino-Lite technology. Statistical analysis using a one-way ANOVA test followed by Tukey’s Honestly Significant Differences test were used to compare the mean pollen counts collected both within and between each sample location. Nostril swabs resulted in yielding the highest mean count of pollen grains while sub-nail swabs yielded the lowest mean count.

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