Spatial modeling of mid-infrared spectral data with thermalcompensation using integrated nested Laplace approximation
2021
The problem of analyzing substances using low-cost sensors with a
low signal-to-noise ratio (SNR) remains challenging. Using accurate models
for the spectral data is paramount for the success of any classification
task. We demonstrate that the thermal compensation of sample heating and
spatial variability analysis yield lower modeling errors than non-spatial
modeling. Then, we obtain the inference of the spectral data probability
density functions using the integrated nested Laplace approximation (INLA)
on a Bayesian hierarchical model. To achieve this goal, we use the fast
and user-friendly R-INLA package in R for the computation. This
approach allows affordable and real-time substance identification with
fewer SNR sensor measurements, thereby potentially increasing throughput
and lowering costs.
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