Analysis of surface plasmon resonance data using a partial least square regression method for glucose concentration estimation

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)2424-2425
Journal / PublicationAnnual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Proceedings
Volume26 IV
Publication statusPublished - 2004
Externally publishedYes

Conference

Title26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004
PlaceUnited States
CitySan Francisco, CA
Period1 - 5 September 2004

Abstract

A wavelength-based surface plasmon resonance (SPR) technique has been used for the measurement of glucose concentration in aqueous solution. Adoption of Partial Least Square (PLS) regression modeling on SPR data with the proposed simple data-pre-treatment method provides a much better model than using traditional minima-hunting with curve-fitting method. PLS gives the prediction error of 27.63 mg/dL with using Unscrambler PLS-toolbox while the traditional method gives an error of 72.15 mg/dL.

Research Area(s)

  • Glucose, Modeling, Partial Least Square, Surface Plasmon Resonance

Bibliographic Note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to lbscholars@cityu.edu.hk.

Citation Format(s)

Analysis of surface plasmon resonance data using a partial least square regression method for glucose concentration estimation. / Chu, L. H.; Zhang, Y. T.; Zhang, C.

In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Proceedings, Vol. 26 IV, 2004, p. 2424-2425.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review