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Identifying predictive markers in complex samples of biogenic volatile compounds using GC×GC-TOFMS and machine learning
Dissertation (MSc (Chemistry))--University of Pretoria, 2022.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2022
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| _version_ | 1867613495028088832 |
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| access_status_str | Open Access |
| author2 | Naudé, Yvette |
| author_browse | Naudé, Yvette |
| author_facet | Naudé, Yvette |
| collection | Thesis |
| dc_rights_str_mv | © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc (Chemistry))--University of Pretoria, 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/88851 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:02.876Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/88851 Identifying predictive markers in complex samples of biogenic volatile compounds using GC×GC-TOFMS and machine learning Naudé, Yvette u27364870@tuks.co.za Rohwer, Egmont Richard Pretorius, Daniel Thomas UCTD GC×GC-TOFMS Volatile organic compounds Machine learning Chemical markers Chemical standards Dissertation (MSc (Chemistry))--University of Pretoria, 2022. Samples of biogenic VOCs are varied and complex, presenting a significant challenge to analytical scrutiny. This dual study investigates the applicability of comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS), in combination with machine learning, in identifying chemical markers — in the form of biogenic volatile organic compounds (VOCs) — as a tool of classification and prediction of discrete biological states. The first study (Identifying predictive volatile markers of genus for southern African Plectranthus and Coleus using GC×GC-TOFMS and machine learning) investigates foliar VOCs as markers of genus for southern African Plectranthus and Coleus species. The second study (Identifying predictive volatile markers of malaria infection from human skin using GC×GC-TOFMS and machine learning) investigates cutaneous VOCs from the human epidermis as markers of malaria-infection. GC×GC-TOFMS was used to analyse the relevant VOC analytes, and three machine learning algorithms (an elastic-net regression, a random forest and a support-vector machine) were used to construct models of the acquired data from a training set, and to make predictions — of genus, in the case of the first study, and on malaria-infection status, in the case of the second study — on samples from a testing set. For the first study (N=45 samples), a predictive accuracy as high as 90% was obtained (with a sensitivity of up to 100%), and a suite of sesquiterpenes (including α- and β-cubebene, β-ylangene, β-copaene, γ-cadinene and isogermacrene D) were identified as putative markers of genus Coleus. Though predictive models were not obtained in the case of the second study (N=52 samples), certain compounds were identified as being potential markers of a participant’s malaria-status. These include alcohols (such as (E)-2-octen-1-ol), sulphur species (such as isoamyl cyanide and isothiazole), and short- to long-chain aliphatic carboxylic acids (such as n-decanoic acid and 9-hexadecenoic acid). Chemistry MSc (Chemistry) Unrestricted 2022-12-22T12:05:16Z 2022-12-22T12:05:16Z 2023 2022 Dissertation * A2023 https://repository.up.ac.za/handle/2263/88851 DOI: https://doi.org/10.25403/UPresearchdata.21603606 https://doi.org/10.25403/UPresearchdata.21603606 en © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD GC×GC-TOFMS Volatile organic compounds Machine learning Chemical markers Chemical standards Identifying predictive markers in complex samples of biogenic volatile compounds using GC×GC-TOFMS and machine learning |
| title | Identifying predictive markers in complex samples of biogenic volatile compounds using GC×GC-TOFMS and machine learning |
| title_full | Identifying predictive markers in complex samples of biogenic volatile compounds using GC×GC-TOFMS and machine learning |
| title_fullStr | Identifying predictive markers in complex samples of biogenic volatile compounds using GC×GC-TOFMS and machine learning |
| title_full_unstemmed | Identifying predictive markers in complex samples of biogenic volatile compounds using GC×GC-TOFMS and machine learning |
| title_short | Identifying predictive markers in complex samples of biogenic volatile compounds using GC×GC-TOFMS and machine learning |
| title_sort | identifying predictive markers in complex samples of biogenic volatile compounds using gc gc tofms and machine learning |
| topic | UCTD GC×GC-TOFMS Volatile organic compounds Machine learning Chemical markers Chemical standards |
| url | https://repository.up.ac.za/handle/2263/88851 https://doi.org/10.25403/UPresearchdata.21603606 |