Mass Spectrometry Data Processing for High-Throughput Sample Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
High throughput sample analysis in mass spectrometry generates large datasets, which are time-consuming to process and can lead to accuracy issues if not handled properly.
Innovation Solution
A system and method for analyzing substance samples that includes a sample ejector, capture probe, nebulizer nozzle, mass analysis instrument, and a data processing system that automatically processes data by correlating intensity peaks with samples, splitting data, and identifying abnormal samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high throughput sample analysis is performed to increase productivity, then the quantity of samples analyzed per unit time increases, but the data processing time and complexity increase significantly
Solution Approach 1:
The patent segments the continuous data stream into discrete sample datasets using acoustic ejection timing information. The data processing system divides the large compilation of sub-datasets into individual sample units, allowing parallel processing and reducing overall processing time. This segmentation enables the system to handle high throughput analysis by breaking down the monolithic data processing task into manageable chunks.
Solution Approach 2:
The patent performs preliminary data processing actions during the acquisition phase. The system correlates intensity peaks with sample timing information in real-time as data is being acquired, rather than waiting until all data collection is complete. This preliminary correlation reduces the computational burden of post-acquisition processing and enables faster turnaround time for high throughput analyses.
2Measurement precision
If manual data processing is performed to ensure accuracy, then data quality improves, but processing time increases and productivity decreases
Solution Approach 1:
The patent implements automated feedback mechanisms where the data processing system continuously validates data quality metrics against predefined criteria. The system provides real-time feedback on data quality, automatically identifies abnormal samples, and triggers appropriate actions without manual intervention. This automated feedback loop maintains high accuracy standards while enabling high throughput processing by eliminating manual review bottlenecks.
Solution Approach 2:
The system performs self-validation and self-correction of data quality issues. The automated data processing system independently identifies abnormal samples, corrects processing errors, and ensures data quality standards are met without requiring manual oversight. This self-service capability maintains measurement precision while dramatically increasing productivity by removing the need for manual data verification.
3Productivity
If automated data processing is implemented to increase productivity, then processing speed improves, but the risk of processing errors increases
Solution Approach 1:
The patent implements beforehand cushioning by incorporating multiple validation checks and error correction mechanisms into the automated data processing pipeline before errors can propagate. The system pre-defines acceptable data quality ranges, validates each processing step, and prepares correction protocols in advance. This proactive error prevention maintains high reliability while enabling automated high-speed processing.
Solution Approach 2:
The automated system incorporates continuous feedback loops that monitor processing quality in real-time. When deviations from expected patterns are detected, the system automatically adjusts processing parameters or flags samples for review. This feedback mechanism ensures that productivity gains from automation do not compromise data reliability, as errors are detected and corrected immediately rather than propagating through the dataset.
4Measurement precision
If complex data processing algorithms are used to improve accuracy, then analyte identification precision improves, but processing time increases
Solution Approach 1:
The patent segments complex data processing tasks into smaller, parallelizable computational units. By dividing the large dataset into individual sample sub-datasets and processing them in parallel, the system maintains high analytical precision while reducing total processing time. This segmentation allows sophisticated algorithms to be applied to smaller data units simultaneously, leveraging multi-core processing capabilities.
Solution Approach 2:
The system dynamically adjusts processing parameters based on data characteristics and computational resource availability. When processing power is abundant, more computationally intensive algorithms are applied for maximum precision. When processing time is constrained, the system optimizes algorithm complexity while maintaining acceptable accuracy thresholds. This parameter flexibility balances precision requirements with processing time constraints.
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
The system enables efficient processing of large datasets from high throughput sample analysis, improving productivity and accuracy by automatically processing data and identifying potential errors.
Implementation Method 1
a sample ejector configured to eject, independently, a plurality of samples from a plurality of wells of a well plate
Implementation Method 2
a nebulizer nozzle configured to receive and ionize the transported diluted samples to produce sample ions
Data Source
AI summary
Methods and systems for mass analysis are disclosed herein. An example system includes: a sample ejector configured to eject a plurality of samples from a plurality of wells of a well plate; a capture probe configured to capture the ejected samples and dilute and transport the captured samples; a nebulizer nozzle configured to receive and ionize the transported diluted samples to produce sample ions; a mass analysis instrument configured to filter and detect ions of interest from the sample ions; a controller configured to coordinate operations of the sample ejector, the capture probe, the nebulizer nozzle, and the mass analysis instrument; and a data processing system configured to acquire data from the mass analysis instrument and conduct an automatic data processing process.