Modified sequential floating selection for blood glucose monitoring using near infrared spectral data

C. F. So; Yugu Zeng; Kup Sze Choi; J. W.Y. Chung; T. K.S. Wong

doi:10.1007/s10812-013-9759-7

Modified sequential floating selection for blood glucose monitoring using near infrared spectral data

C. F. So, Yugu Zeng, Kup Sze Choi, J. W.Y. Chung, T. K.S. Wong

Research output: Journal article publication › Journal article › Academic research › peer-review

Abstract

To enable non-invasive blood glucose monitoring using near infrared spectroscopy, a modified sequential floating selection method is proposed to remove uninformative data from the spectrum. A linear discriminant function is then used for classification based on selected features. Experiments show that this approach is able to give promising prediction results by classifying near infrared spectroscopic data of blood glucose with good accuracy.

Original language	English
Pages (from-to)	284-288
Number of pages	5
Journal	Journal of Applied Spectroscopy
Volume	80
Issue number	2
DOIs	https://doi.org/10.1007/s10812-013-9759-7
Publication status	Published - 1 May 2013

Keywords

Linear discriminant function
Near infrared
Sequential floating selection
Spectroscopic data

ASJC Scopus subject areas

Condensed Matter Physics
Spectroscopy

More information

10.1007/s10812-013-9759-7

Cite this

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abstract = "To enable non-invasive blood glucose monitoring using near infrared spectroscopy, a modified sequential floating selection method is proposed to remove uninformative data from the spectrum. A linear discriminant function is then used for classification based on selected features. Experiments show that this approach is able to give promising prediction results by classifying near infrared spectroscopic data of blood glucose with good accuracy.",

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AU - Zeng, Yugu

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AU - Chung, J. W.Y.

AU - Wong, T. K.S.

PY - 2013/5/1

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AB - To enable non-invasive blood glucose monitoring using near infrared spectroscopy, a modified sequential floating selection method is proposed to remove uninformative data from the spectrum. A linear discriminant function is then used for classification based on selected features. Experiments show that this approach is able to give promising prediction results by classifying near infrared spectroscopic data of blood glucose with good accuracy.

KW - Linear discriminant function

KW - Near infrared

KW - Sequential floating selection

KW - Spectroscopic data

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DO - 10.1007/s10812-013-9759-7

M3 - Journal article

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JO - Journal of Applied Spectroscopy

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SN - 0021-9037

IS - 2

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Modified sequential floating selection for blood glucose monitoring using near infrared spectral data

Abstract

Keywords

ASJC Scopus subject areas

More information

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