This thesis sets out to address the challenges with the comparison of Amphetamine material in determining whether they originate from the same source or different sources using pairwise ratios of peak areas within each chromatogram of material and then modeling the difference between the ratios for each comparison as a basis for evaluation. The evaluation of an existing method that uses these ratios to determine the sum of significant differences between each comparison of material that is provided is done. The outcome of this evaluation suggests that there the distributions for comparison of samples originating from the same source and the comparison of samples originating from different sources have an overlap leading to uncertainties in conclusions. In this work, the differences between the ratios of peak areas have been modeled using a feature-based approach. Because the feature space is quite large, Discriminant Analysis methods such as Linear Discriminant Analysis (LDA) and Partial least squares Discriminant Analysis (PLS-DA) have been implemented to perform classification by dimensionality reduction. Another popular method that works on the principle of nearest centroid classifier called as Nearest shrunken centroid is also applied that performs classification on shrunken centroids of the features. The results and analysis of all the methods have been performed to obtain the classification results for classes +1 (samples originate from the same source) and ́1 (samples originate from different sources). Likelihood ratios of each class for each of these methods have also been evaluated using the Empirical Cross-Entropy (ECE) method to determine the robustness of the classifiers. All three models seem to have performed fairly well in terms of classification with LDA being the most robust and reliable with its predictions.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-176416 |
| Date | January 2021 |
| Creators | Pawar, Yash |
| Publisher | Linköpings universitet, Statistik och maskininlärning |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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