Khadem, H. orcid.org/0000-0002-6878-875X, Nemat, H., Elliott, J. orcid.org/0000-0002-7867-9987 et al. (1 more author) (2022) Signal fragmentation based feature vector generation in a model agnostic framework with application to glucose quantification using absorption spectroscopy. Talanta, 243. 123379. ISSN 0039-9140
Abstract
This paper proposes feature vector generation based on signal fragmentation equipped with a model interpretation module to enhance glucose quantification from absorption spectroscopy signals. For this purpose, near-infrared (NIR) and mid-infrared (MIR) spectra collected from experimental samples of varying glucose concentrations are scrutinised. Initially, a given spectrum is optimally dissected into several fragments. A base-learner then studies the obtained fragments individually to estimate the reference glucose concentration from each fragment. Subsequently, the resultant estimates from all fragments are stacked, forming a feature vector for the original spectrum. Afterwards, a meta-learner studies the generated feature vector to yield a final estimation of the reference glucose concentration pertaining to the entire original spectrum. The reliability of the proposed approach is reviewed under a set of circumstances encompassing modelling upon NIR or MIR signals alone and combinations of NIR and MIR signals at different fusion levels. In addition, the compatibility of the proposed approach with an underlying preprocessing technique in spectroscopy is assessed. The results obtained substantiate the utility of incorporating the designed feature vector generator into standard benchmarked modelling procedures under all considered scenarios. Finally, to promote the transparency and adoption of the propositions, SHapley additive exPlanations (SHAP) is leveraged to interpret the quantification outcomes.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Elsevier B.V. This is an author produced version of a paper subsequently published in Talanta. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Glucose quantification; Near-infrared spectroscopy; Mid-infrared spectroscopy; Machine learning; SHAP |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) The University of Sheffield > Sheffield Teaching Hospitals |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 Mar 2022 09:01 |
Last Modified: | 15 Mar 2023 01:13 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.talanta.2022.123379 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184918 |