Selectivity Assessment of Noninvasive Glucose Measurements Based on Analysis of Multivariate Calibration Vectors

Mark A Arnold; Lingzhi Liu; Jonathon T Olesberg

doi:10.1177/193229680700100402

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Selectivity Assessment of Noninvasive Glucose Measurements Based on Analysis of Multivariate Calibration Vectors

Journal article

Open access

Peer reviewed

Selectivity Assessment of Noninvasive Glucose Measurements Based on Analysis of Multivariate Calibration Vectors

Mark A Arnold, Lingzhi Liu and Jonathon T Olesberg

Journal of diabetes science and technology, Vol.1(4), pp.454-462

07/2007

DOI: 10.1177/193229680700100402

PMCID: PMC2769645

PMID: 19885107

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Abstract

Background: Selectivity is paramount for the successful implementation of noninvasive spectroscopic sensing for the painless measurement of blood glucose concentrations in people with diabetes. Selectivity issues are explored for different multivariate calibration models based on noninvasive near-infrared spectra collected from an animal model. Methods: Noninvasive near-infrared spectra are collected through a fiber-optic interface attached to a thin fold of skin on the back of an anesthetized laboratory rat while glucose levels are varied in a controlled manner. Results and Discussion: Partial least-squares (PLS) calibration models are generated from noninvasive spectra collected during a single, 2-hour blood glucose transient. Calibration vectors are compared for optimized PLS calibration models created with correct and incorrect assignments of glucose concentrations to each noninvasive spectrum. Although both PLS models appear functional and seem capable of predicting glucose concentrations accurately during this transient, only the model generated from correct glucose assignments gives a credible calibration vector. When correct glucose assignments are used, the PLS calibration vector matches the corresponding net analyte signal calibration vector. No similarity in these calibration vectors is evident when incorrect glucose assignments are used. Conclusions: Glucose-specific spectral information is present within noninvasive near-infrared spectra collected from a rat model using a transmission geometry. Apparently functional, yet incorrect, calibration models can be generated, and the propensity to create such false PLS calibration models calls into question the validity of past reports. An analysis of calibration vectors can provide valuable insight into the chemical basis of selectivity for multivariate calibration models of complex systems.

near-infrared spectroscopy

calibration vector analysis

Clarke error grid analysis

noninvasive glucose sensing

net analyte signal

multivariate statistics

Details

Title: Subtitle: Selectivity Assessment of Noninvasive Glucose Measurements Based on Analysis of Multivariate Calibration Vectors
Creators: Mark A Arnold - Department of Chemistry and Optical Science and Technology Center, University of Iowa, Iowa City, Iowa
Lingzhi Liu - Department of Chemistry and Optical Science and Technology Center, University of Iowa, Iowa City, Iowa
Jonathon T Olesberg - Department of Chemistry and Optical Science and Technology Center, University of Iowa, Iowa City, Iowa
Resource Type: Journal article
Publication Details: Journal of diabetes science and technology, Vol.1(4), pp.454-462
DOI: 10.1177/193229680700100402
PMID: 19885107
PMCID: PMC2769645
NLM abbreviation: J Diabetes Sci Technol
ISSN: 1932-2968
eISSN: 1932-2968
Publisher: SAGE Publications
Language: English
Date published: 07/2007
Academic Unit: Physics and Astronomy; Center for Biocatalysis and Bioprocessing; Fraternal Order of Eagles Diabetes Research Center; Chemistry
Record Identifier: 9984216670002771

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