Improving liquid chromatography-tandem mass spectrometry determinations by modifying noise frequency spectrum between...

Area of Science:

• Analytical Chemistry
• Chemometrics
• Spectrometry

Background:

• Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is crucial for analyzing complex samples.
• Improving signal-to-noise (S/N) ratios in LC-MS/MS is essential for accurate quantification, especially at low concentrations.
• Existing low-pass filtering methods, like matched filters, can cause peak broadening, while Fourier transform filters may distort signals.

Purpose of the Study:

• To develop a novel chemometric technique to enhance the peak signal-to-noise (S/N) ratio in LC-MS/MS.
• To improve upon existing wavelet-based low-pass filtering methods by modifying the noise spectrum.
• To avoid signal distortion and peak broadening issues associated with other filtering techniques.

Main Methods:

• Employed a chemometric technique involving modifying the noise spectrum between two consecutive wavelet-based low-pass filter procedures.
• Utilized a multiplication alteration of a wavelet-based low-pass filtered LC-MS/MS chromatogram with an artificial chromatogram containing thermal noise.
• Applied sequential convolution with low-pass filters to obtain approximation coefficients at multiple resolution levels.

Main Results:

• Achieved significant peak S/N ratio improvements, typically ranging from 25-fold to 40-fold, compared to 6-fold with standard consecutive wavelet filters.
• Demonstrated that the noise frequency spectrum is shifted towards higher frequencies by the multiplication alteration, enhancing subsequent filter efficiency.
• Validated the linearity of standard curves and reproducibility of filtered LC-MS/MS signals, enabling more accurate determination of very low concentration samples.

Conclusions:

• The proposed noise modification technique effectively enhances S/N ratios in LC-MS/MS without peak broadening or signal distortion.
• This method offers a substantial improvement over conventional single-step or consecutive wavelet-based filtering for trace analysis.
• The validated filtered signals provide more accurate quantification of low-concentration analytes in LC-MS/MS applications.