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Monte Carlo Wavelet Analysis for Objective Peak Detection in SRM LC-MS/MS Analysis
Randall K Julian1,2, Brian A Rappold3,4, Fan Yi5
1Indigo BioAutomation, Inc., Carmel, Indiana 46032, United States.
Analytical Chemistry
|June 12, 2026
Summary
A new wavelet-based Monte Carlo method objectively identifies low-level analytes in complex mass spectrometry data. This approach distinguishes true peaks from noise, improving accuracy in detecting substances like ketamine in clinical samples.
Area of Science:
- Analytical Chemistry
- Biochemistry
- Computational Biology
Background:
- Accurate detection of low-level analytes in complex chromatographic-mass spectrometric data is challenging.
- Conventional signal-to-noise ratios fail due to structured chemical noise and coeluting interferences.
- A robust statistical criterion is needed to differentiate true peaks from background noise.
Purpose of the Study:
- To introduce a wavelet-based Monte Carlo technique for statistically validating Selected Reaction Monitoring Liquid Chromatography-Tandem Mass Spectrometry (SRM LC-MS/MS) peaks.
- To develop an objective and reproducible method for peak integration decisions in complex datasets.
- To address limitations of traditional signal-to-noise criteria in the presence of structured chemical noise.
Main Methods:
- Developed a wavelet-based Monte Carlo approach to characterize chemical noise.
- Constructed a generative noise-only null model using Monte Carlo resampling.
- Controlled for family-wise error rate (FWER) to assign statistically significant p values.
- Validated the method using SRM dilution series in plasma and a clinical pain panel.
Main Results:
- Peaks with adjusted p values < 0.05 correctly identified true positives above the limit of detection.
- The method accurately classified matrix blanks and biological negatives below the limit of detection.
- Successfully detected ketamine in confirmed positive samples below the lowest calibration standard.
- Demonstrated application on a lipid mediator dataset, replacing subjective noise selection with formal hypothesis testing.
Conclusions:
- The wavelet-based Monte Carlo method provides a statistically rigorous and objective criterion for peak detection in LC-MS/MS.
- This approach enhances the reliability of analyte quantification, particularly at low concentrations.
- The method offers improved performance over conventional signal-to-noise ratios in complex matrices.
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