Journal article
Use of Constant-Analyte Samples in Updating Quantitative Near-Infrared Calibration Models for Physiological Levels of Glucose
Analytical letters, Vol.58(8), pp.1260-1274
05/24/2025
DOI: 10.1080/00032719.2024.2361331
Abstract
Updating procedures were investigated for multivariate calibration models for physiological levels of glucose in aqueous samples based on near-infrared spectroscopy. The methodology evaluated used samples with constant glucose concentrations collected on the prediction day to augment previously collected calibration spectra for the purpose of computing an updated model for use only on that day. Models were based on partial-least squares (PLS) regression in conjunction with spectral preprocessing using direct orthogonal signal correction (DOSC). The use of constant-analyte samples allowed the procedure to be compatible with samples collected at the beginning of a chemical reaction or from a fasting patient in a blood glucose monitoring application. The methodology was tested with nine sets of prediction spectra collected from 368 to 617 days after the collection of the calibration data. The updated PLS models based on four augmented samples exhibited an approximately 50% improvement in prediction accuracy relative to models that were based solely on the original calibration data. The use of DOSC in conjunction with updating was found to be beneficial overall, but only led on average to less than 10% improvement in prediction performance.
Details
- Title: Subtitle
- Use of Constant-Analyte Samples in Updating Quantitative Near-Infrared Calibration Models for Physiological Levels of Glucose
- Creators
- Austin J. Gessell - University of IowaGary W. Small - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Analytical letters, Vol.58(8), pp.1260-1274
- DOI
- 10.1080/00032719.2024.2361331
- ISSN
- 0003-2719
- eISSN
- 1532-236X
- Publisher
- Taylor & Francis
- Number of pages
- 15
- Grant note
- Samsung Advanced Institute of Technology; Samsung
- Language
- English
- Electronic publication date
- 06/04/2024
- Date published
- 05/24/2025
- Academic Unit
- Chemistry
- Record Identifier
- 9984649057302771
Metrics
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