A STUDY OF THE CLASSIFICATION AND QUANTIFICATION OF MICROPLASTICS THROUGH RAMAN SPECTROSCOPY AND MACHINE LEARNING

Abstract

Synthetic polymers, or ‘plastics’, have become an unavoidable, necessary and ubiquitous part of modern human life. Their low cost, tunable properties, durability and ease of manufacture have led to plastics use in almost every part of day-to-day life including food packaging, car manufacturing and single-use sterile medical equipment. The durability of these plastics, while an advantage in their operational life, results in substantial longevity upon their disposal. After they have been discarded, many plastic particles can exist for up to 1000 years in the environment before their eventual breakdown. Their continued use and disposal as the global population increases have led to a large accumulation of discarded plastics throughout the world. A substantial amount of these exist in sizes of 5 mm or less, and are classified as ‘microplastics’, small pervasive pollutants that have been detected in food, drink, and inside human bodies. It is necessary for researchers to determine suitable ways of characterising and quantifying microplastic particles to increase understanding of their behavior and makeup. Expanding knowledge of the sources, abundance and variety of these particles within the environment can lead to a more comprehensive understanding of the issue of microplastics pollution. The use of Raman spectroscopy as an analytical technique for classification of microplastics particles has emerged as an efficient and accurate tool for characterisation. Traditional validation of Raman spectra using library searches and comparison to reference spectra, however, is often inadequate when the plastics have faced significant environmental degradation, which can alter their Raman spectrum. Alternative validation methods such as machine learning are becoming more widely used as superior techniques to traditional library searches, and have proven fast, effective and cheap ways to identify microplastic particles from their Raman spectra. This thesis describes iterative development of machine learning based methods to semi-automate identification of microplastic particles using Raman spectroscopy.