AEMS Peak Deconvolution for Overlapping Sample Ejection Signals
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Acoustic Ejection Mass Spectrometry (AEMS) systems face limitations in analytical throughput due to broad peak widths, which hinder faster analysis speeds, especially when using standard carrier solvents and hardware configurations, making it challenging to achieve higher throughput for certain assays.
Innovation Solution
A method and system for determining convolved peak intensity in AEMS by generating an ejection time log, analyzing sample ejections, and using known peak shapes, distribution functions, and processor operations to estimate peak positions and shapes, allowing for improved peak deconvolution and intensity calculation, even with overlapping signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard carrier solvent and hardware configuration are used, then system robustness is maintained, but analytical throughput is limited due to broad peak widths
Solution Approach 1:
The system performs preliminary deconvolution calculations using stored peak shape templates before final analysis. By pre-characterizing peak shapes under various conditions and storing them as templates, the system can rapidly deconvolve overlapping peaks during high-throughput analysis without requiring real-time complex calculations, thus improving throughput while maintaining precision
Solution Approach 2:
The system creates digital copies of peak shapes under standardized conditions and stores them as templates. These template copies are then reused for deconvolution during high-throughput analysis, eliminating the need to re-measure peak shapes for each analysis and enabling faster processing while maintaining consistent precision standards
2Productivity
If faster sampling rates are used, then analytical throughput increases, but peak overlap increases making intensity determination difficult
Solution Approach 1:
The system uses an iterative feedback approach where initial peak intensity estimates are refined through multiple deconvolution cycles. The deconvolution algorithm continuously adjusts peak parameters based on the difference between observed and calculated signals, converging to accurate intensity values even for heavily overlapping peaks at high sampling rates
Solution Approach 2:
The system segments the convolved peak signal into individual contributing peaks by identifying characteristic features and applying deconvolution algorithms. This segmentation separates overlapping peak contributions mathematically, allowing accurate intensity determination for each individual peak even when they overlap significantly in the time domain
3Speed
If shorter delay times between samplings are used, then analysis speed increases, but peak convolution increases reducing measurement accuracy
Solution Approach 1:
The system pre-stores peak shape templates characterized under various delay time conditions. When analyzing data with short delay times, the appropriate pre-characterized templates are selected and applied, eliminating the need for real-time peak shape measurement and enabling accurate deconvolution even when peaks are heavily convolved due to short sampling intervals
Solution Approach 2:
The system replaces physical separation methods (which would require longer delay times) with computational deconvolution methods. By using mathematical algorithms to separate overlapping peaks in the digital domain, the system achieves accurate intensity measurements without requiring physical temporal separation, thus maintaining measurement precision while enabling faster analysis speeds
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances analytical throughput by accurately determining peak intensities and shapes, even with short delay times between samplings, improving peak area calculations and resolving overlapping peaks, thereby increasing the speed and precision of AEMS analysis.
Implementation Method 1
Acoustic Ejection Mass Spectrometry (AEMS) is a high-throughput analytical platform, where nano-liter sized sample droplets are ejected acoustically from a sample well plate
Data Source
AI summary
A method for determining a convolved peak intensity in a sample trace includes ejecting a plurality of sample ejections from a sample well plate. An ejection time log is generated which includes an ejection time of each of the plurality of sample ejections from the sample well plate. The plurality of sample ejections is analyzed with a mass analyzer. The sample trace of intensity versus time values is produced for the plurality of sample ejections based on the analysis. A known peak shape is obtained. A convolved peak intensity is determined for a convolved peak of the sample trace based at least in part on the known peak shape and the ejection time log.