Noninvasive Temperature Measurements in Tissue-Simulating Phantoms Using a Solid-State Near-Infrared Sensor.

Area of Science:

• Biomedical Engineering
• Spectroscopy
• Wearable Technology

Background:

• Current wearable temperature sensors measure skin surface temperature, which is influenced by ambient conditions.
• A more accurate, physiological body temperature measurement is needed for effective health monitoring.

Purpose of the Study:

• To demonstrate the feasibility of noninvasive body temperature measurement using near-infrared spectroscopy (NIRS).
• To develop and evaluate calibration models for predicting temperature from NIRS data in a controlled in vitro setting.

Main Methods:

• Utilized a miniaturizable solid-state laser-diode-based NIRS spectrometer.
• Collected diffuse reflectance spectra from seven tissue phantoms with varying compositions (water, gelatin, Intralipid) at temperatures between 20-24 °C.
• Developed two partial least squares (PLS) calibration models to predict temperature from spectral data.

Main Results:

• The first PLS model (calibration/prediction split) achieved a coefficient of determination (R²) of 0.95 and a standard error of prediction (SEP) of 0.22 °C.
• The second PLS model (leave-one-phantom-out) showed R² values from 0.67-0.99 and SEP values from 0.19-0.65 °C across seven phantom-specific models.
• Identified sample-to-spectrometer interface stability and reproducibility as key sources of spectral variance.

Conclusions:

• NIRS shows significant potential for noninvasive, continuous body temperature monitoring.
• This in vitro study provides a strong foundation for developing in vivo wearable temperature sensing technologies.
• Future research should focus on addressing spectral variance for improved accuracy in real-world applications.