The application of multivariate analysis to aid interpretation of textile fibre dyes analysed by microspectrophotometry

Abstract

There have been numerous recent publications calling for an increase in the reliability of forensic evidence. Furthermore, there have been comments on a noticeable lack of research published with regard to the application of multivariate analysis to textile fibre evidence. In this work a classification system was proposed that would utilise a probabilistic approach, require minimal user input, and be robust. The system utilised microspectrophotometry data collected from various fibres - without the use of additional analytical techniques such as microscopy or thin layer chromatography to represent a more streamlined and objective approach. A set of optimal settings for the classification system were established through experimentation utilising acrylic and cotton fibres from indistinguishable and distinguishable sources. In addition, two multivariate analysis approaches were investigated; the application of principal component analysis for dimension reduction followed by linear discriminant analysis (PCA-LDA) and the utilisation of linear discriminant analysis alone (LDA-own). The optimal settings for the proposed classification system were found to be upper/lower self-predictive probability (SPP) = 0.9999/0.0001, exceedance proportion (EP) = 0.5 and number of fibres per group = 10. Up to 100% classification accuracy was observed when considering both fibres from indistinguishable and distinguishable sources – provided that 10 fibres were available from both sources and that the dye composition of both sources were suitably dissimilar if they were truly from different sources. If only single fibres were available for analysis, or the dye composition between truly different sources of fibres was too similar then classification accuracy decreased.

Date of Award	15 Jan 2021
Original language	English
Awarding Institution	Northumbria University
Supervisor	Kelly Sheridan (Supervisor), Martin Evison (Supervisor) & Guangquan Li (Supervisor)