Paper
11 November 2005 Multivariate calibration with slowly responding reference measurements
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Proceedings Volume 6007, Smart Medical and Biomedical Sensor Technology III; 60070O (2005) https://doi.org/10.1117/12.630701
Event: Optics East 2005, 2005, Boston, MA, United States
Abstract
The application of multivariate calibration models, specifically those using partial least squares (PLS) regression to relate near infrared (NIR) spectral data to analyte concentrations, relies upon accurate knowledge of the concentrations during model building. In a physiologic system, such as human skeletal muscle, these concentrations can be measured using invasive sensors which may have material properties that limit diffusion of analytes to the sensing chemistry, thus taking several minutes to fully respond to an analyte change which actually occurs in seconds. This results in a poor time correlation between reference measurements of analyte concentrations and spectral data, which in turn degrades the performance of the PLS model. We mathematically modeled the response of an invasive sensor measurement and used this response to develop a filter to time-match the raw NIR spectra before building the PLS model. PLS models for interstitial pH in exercising human flexor digitorum profundus muscle were developed with and without the time-matching filter. In a single exercising subject, root mean square error of prediction (RMSEP) = 0.05 pH units and r2 = 0.39 without filtering, but improved to RMSEP = 0.02 pH units with r2 = 0.91 after the time-matching filter was implemented. The time-matching filter was shown to be effective in improving model performance when spectral response is more rapid than the invasive sensor reference measurement.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael A. Shear, Olusola O. Soyemi, Michelle Landry, and Babs R. Soller "Multivariate calibration with slowly responding reference measurements", Proc. SPIE 6007, Smart Medical and Biomedical Sensor Technology III, 60070O (11 November 2005); https://doi.org/10.1117/12.630701
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Cited by 2 scholarly publications.
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KEYWORDS
Sensors

Data modeling

Calibration

Absorbance

Tissues

Optical filters

Mathematical modeling

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