Patchava, K.C., Benaissa, M., Malik, B. et al. (1 more author) (2015) Local linear embedded regression in the quantitative analysis of glucose in near infrared spectra. Analytical Methods, 7 (4). 1484 - 1492. ISSN 1759-9660
Abstract
This paper investigates the use of Local Linear Embedded Regression (LLER) for the quantitative analysis of glucose from near infrared spectra. The performance of the LLER model is evaluated and compared with the regression techniques Principal Component Regression (PCR), Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) both with and without pre-processing. The prediction capability of the proposed model has been validated to predict the glucose concentration in an aqueous solution composed of three components (urea, triacetin and glucose). The results show that the LLER method offers improvements in comparison to PCR, PLSR and SVR.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Royal Society of Chemistry 2015. This is an author produced version of a paper subsequently published in Analytical Methods. Uploaded in accordance with the publisher's self-archiving policy. |
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) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 28 Jan 2016 15:36 |
Last Modified: | 08 Mar 2016 19:26 |
Published Version: | https://dx.doi.org/10.1039/c4ay02874k |
Status: | Published |
Publisher: | Royal Society of Chemistry |
Refereed: | Yes |
Identification Number: | 10.1039/c4ay02874k |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:92964 |