A Bayesian Approach to Binary Classification of Mid-Infrared Spectral Data With Noisy Sensors

摘要: The problem of classifying substances using MIR laser and sensors with low signal-to-noise ratio remains challenging. existing methods rely largely on lasers at multiple wavelengths expensive high quality sensors. We propose demonstrate a statistical method that classifies spectral data generated from imaging spectroscopy experiments few inexpensive detector arrays while still achieving accuracy. Results quantifiable analytic performance are obtained by attributing probability distribution functions to the images implementing binary decision process. Our can provide solution as single measurement allows use SNR This increase throughput lower costs security checkpoints, pharmaceutical production monitoring, industrial control, similar applications.