Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Applied Spectroscopy
  • Vol. 45,
  • Issue 4,
  • pp. 546-554
  • (1991)

Signal-to-Noise Considerations in Flame/Furnace Infrared Emission Spectroscopy

Not Accessible

Your library or personal account may give you access

Abstract

A theoretical model for the signal-to-noise ratio (SNR) performance of a flame/furnace infrared emission (FIRE) radiometer using a PbSe detector has been developed. The model uses readily available, published PbSe detector parameters to predict the SNR for situations involving only detector noise as well as for situations involving detector noise in the presence of residual, uncorrelated flame noise remaining after background subtraction. The theoretical SNR values were found to be in good agreement (6%) with previously published, experimentally determined SNR values for Freon-113®, obtained with a dual-channel FIRE radiometer operating in the carbon mode. The model was used to show the effect of source modulation frequency on the SNR obtained with the FIRE radiometer. At room temperature under detector-noise-limited conditions, it was found that: 1/<i>f</i> noise > background photon noise > generation-recombination noise ≫ Johnson noise. The PbSe detector was found to exhibit 1/<i>f</i>-noise behavior out to about 1000 Hz when operated at room temperature. The model predicts that, even for a dual-channel FIRE radiometer in the presence of residual flame background noise, some SNR improvement should be possible by increasing the chopping frequency out to around 3 kHz.

PDF Article
More Like This

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.