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

L H Chu¹, Y T Zhang, C Zhang

¹Dept. of Electron. Eng., Chinese Univ. of Hong Kong,China.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|February 3, 2007

Summary

Partial Least Square (PLS) regression modeling improves glucose concentration measurement using surface plasmon resonance (SPR) technology. This advanced method significantly reduces prediction error compared to traditional techniques, enhancing diagnostic accuracy for glucose monitoring.

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Area of Science:

Analytical Chemistry
Biomedical Engineering
Spectroscopy

Background:

Accurate glucose concentration measurement is crucial for diabetes management.
Surface Plasmon Resonance (SPR) is a label-free optical sensing technique with potential for glucose monitoring.
Traditional data analysis methods for SPR can be limited in accuracy.

Purpose of the Study:

To evaluate the effectiveness of Partial Least Square (PLS) regression modeling for analyzing SPR data in glucose concentration measurements.
To compare the performance of PLS regression against traditional minima-hunting with curve-fitting methods.
To develop a more accurate and reliable method for quantifying glucose in aqueous solutions using SPR.

Main Methods:

A wavelength-based Surface Plasmon Resonance (SPR) system was employed for measurements.
SPR data underwent a simple data-pretreatment procedure.
Partial Least Square (PLS) regression modeling was applied to the pretreated SPR data.
Results were compared with those obtained using traditional minima-hunting and curve-fitting techniques.

Main Results:

PLS regression modeling, combined with simple data pretreatment, significantly improved the accuracy of glucose concentration measurements.
The PLS method achieved a prediction error of 27.63 mg/dL.
The traditional minima-hunting with curve-fitting method resulted in a higher prediction error of 72.15 mg/dL.

Conclusions:

Partial Least Square (PLS) regression offers a superior approach for analyzing SPR data in glucose sensing applications.
The proposed data-pretreatment and PLS modeling strategy enhances the precision and reliability of glucose concentration determination.
This advancement holds promise for improved non-invasive glucose monitoring devices.