Spatial modeling of mid-infrared spectral data with thermal compensation using integrated nested Laplace...

Area of Science:

• Analytical Chemistry
• Sensor Technology
• Data Science

Background:

• Analyzing substances with low-cost sensors is hindered by low signal-to-noise ratio (SNR).
• Accurate spectral data modeling is crucial for effective substance classification.
• Existing methods may not sufficiently address thermal effects or spatial variations in sensor readings.

Purpose of the Study:

• To improve substance analysis accuracy using low-cost sensors with low SNR.
• To investigate the impact of thermal compensation and spatial variability on spectral modeling.
• To implement a Bayesian hierarchical model for spectral data inference.

Main Methods:

• Applied thermal compensation of sample heating and spatial variability analysis.
• Utilized integrated nested Laplace approximation (INLA) for Bayesian hierarchical modeling.
• Employed the R-INLA package in R for efficient computation of probability density functions.

Main Results:

• Thermal compensation and spatial variability analysis resulted in lower modeling errors compared to non-spatial approaches.
• The Bayesian hierarchical model successfully inferred spectral data probability density functions.
• The developed approach demonstrated potential for real-time substance identification.

Conclusions:

• Integrating thermal compensation and spatial analysis enhances spectral modeling for low-cost sensors.
• Bayesian hierarchical modeling with R-INLA provides an effective framework for analyzing low SNR spectral data.
• This method offers a pathway to affordable, real-time substance identification, reducing measurement needs and costs.