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Submultiple Data Collection to Explore Spectroscopic Instrument Instabilities Shows that Much of the "Noise" is not
Curtis W Meuse1, James J Filliben2, Kenneth A Rubinson3,4
1Institute for Bioscience and Biotechnology Research of the University of Maryland and the Biomolecular Measurement Division , National Institute of Standards and Technology , Rockville , Maryland 20850 , United States.
Analytical Chemistry
|April 4, 2018
Summary
New spectroscopic analysis reveals that signal fluctuations contain "fast drift" beyond stochastic noise. Using submultiple medians improves signal-to-noise ratio and reduces bias in spectrometric data.
Area of Science:
- Spectrometry
- Data Analysis
- Signal Processing
Background:
- Traditional spectrometric noise reduction relies on time averaging, assuming noise decreases with the square root of collection time.
- Contemporary data collection capabilities allow for more detailed signal analysis beyond simple averaging.
Purpose of the Study:
- To investigate the nature of signal fluctuations in spectrometry beyond stochastic noise.
- To develop a method for improving signal-to-noise ratio and reducing bias in spectroscopic measurements.
Main Methods:
- Analyzing signal fluctuations over submultiples of the total data collection time.
- Utilizing autocorrelations of submultiple signal sets to differentiate noise from fast drift.
- Applying median calculations to distributions of signal fluctuations to mitigate sampling bias.
Main Results:
- Identified a significant component of
- fast drift
- in signal fluctuations, previously mistaken for stochastic noise.
- Demonstrated that signal fluctuations are often unbalanced around the mean, leading to sampling bias.
- Showed that using medians of submultiple signal distributions increases signal-to-noise ratio and reduces bias.
Conclusions:
- Spectrometric data analysis can be enhanced by accounting for fast drift and signal imbalance.
- The submultiple median data treatment offers a robust method for improving spectral quality.
- This technique is effective across various spectroscopic methods, including infrared, circular dichroism, and Raman spectrometry.
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