Analysis of Fasting Skin Spectra to Characterize Background Spectral Variance for Non-Invasive Glucose Sensing in People
Authors' conclusion
This preliminary report proposes a fasting protocol suitable for characterizing the BSV for noninvasive near infrared spectra. Net analyte signal calibration models are used to illustrate how unaccounted for BSV negatively impacts analytical accuracy.
Study context
- Population
- People; transmission noninvasive skin spectra over the combination region of the near infrared spectrum, collected under fasting conditions
- Outcome
- Characterization of background spectral variance (BSV) for noninvasive glucose sensing
Methodological detailspreliminary report, Spectra collected continuously over 270 minutes under fasting conditions, Glucose transient created by linearly adding concentration-scaled pure component glucose spectra to each fasting spectrum, Net analyte signal calibration models built with different subsets of the fasting spectra
- Preliminary report
- Spectra collected continuously over 270 minutes under fasting conditions
- Glucose transient created by linearly adding concentration-scaled pure component glucose spectra to each fasting spectrum
- Net analyte signal calibration models built with different subsets of the fasting spectra
Main finding
As reported in the abstract
Noise level of fasting spectra (root mean square noise analysis of 100% lines)
26 µAU
Original publication
Analysis of Fasting Skin Spectra to Characterize Background Spectral Variance for Non-Invasive Glucose Sensing in People. J Diabetes Sci Technol. Published 12 August 2026. doi:10.
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