Ensemble Averaging
- Page ID
- 77546
Ensemble averaging is a data acquisition method that enhances the signal-to-noise of an analytical signal through repetitive scanning. Ensemble averaging can be done in real time, which is extremely useful for analytical methods such as:
- Nuclear Magnetic Resonance Spectroscopy (NMR)
- Fourier Transform Infrared Spectroscopy (FTIR)
- Near-Infrared (NIR) Spectrophotometry
- UV-Visible Spectrophotometry
Ensemble averaging also works well with multiple datasets once data acquisition is complete. In either case, this method of S/N enhancement requires that:
- The analyte signal must be stable
- The source of noise is random
How Ensemble Averaging Works
- Repeated experiments (scans) are performed on the chemical system in question. The scans are averaged either in real-time or after the data acquisition is complete. A visualization of this process is shown below for five spectra of 8.8 μg/mL 1,1'-ferrocenedimethanol in water.
Pros of Ensemble Averaging
- Ensemble averaging filters out random noise, regardless of the noise frequency
- Ensemble averaging is effective, even if the original signal has a S/N<1
- Ensemble averaging is straightforward to implement
- Improvement in S/N is proportional to:
\[\sqrt{\#\textrm{ of datasets averaged together}}\]
Cons of Ensemble Averaging
- Requirement of a stable signal
- Ensemble averaging will not work if noise is not random (e.g. 60 Hz electrical noise)
Example of Ensemble Averaging
These simulated 5-μV gaussian signals illustrate S/N improvement of ensemble averaging. The bottom dataset represents a S/N of 2 (single dataset), the middle dataset represents a S/N of 8 (average of 16 datasets), and the top dataset represents a S/N of 20 (average of 100 datasets).
Click here to work on an ensemble averaging exercise.