TOF MS Data Processing for Microbial Identification
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Solution Overview
Problem
Current time-of-flight mass spectrometry (TOF MS) methods face challenges in accurately identifying microbial species, particularly when multiple species are present, due to non-distinctive peak intensity profiles in ribosomal protein spectra, leading to potential errors and incorrect identifications, as seen in cases like Bacillus cereus and Bacillus thuringiensis.
Innovation Solution
The proposed solution involves determining a second molecular weight range, specifically between 500 to 3000 Da or an m/z value of 500 to 1000, which differentiates between candidate microorganisms based on their peak intensities, allowing for accurate identification through secondary processing in the TOF MS data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If general peak intensity analysis in mass spectrum is used for microbial identification, then the identification process is simple, but the identification accuracy is low and cannot distinguish between similar microorganisms
Solution Approach 1:
The patent segments the mass spectrum data processing into multiple stages: initial peak intensity analysis to generate candidate microorganism groups, followed by secondary analysis using optimized molecular weight ranges and peak intensity ratios for precise identification. This segmentation allows the system to maintain simplicity for clear cases while applying complexity only when needed for ambiguous identifications.
Solution Approach 2:
The patent transitions from one-dimensional peak intensity analysis to multi-dimensional analysis by incorporating molecular weight ranges, peak intensity ratios, and comparative spectral patterns. This dimensional expansion enables differentiation between microorganisms that appear similar in conventional analysis.
2Reliability
If a broad molecular weight range is analyzed for all candidate microorganisms, then comprehensive coverage is achieved, but data processing time and computational load increase
Solution Approach 1:
The patent divides the molecular weight analysis into segments: a broad initial range for candidate generation, followed by a focused secondary range (500-3000 Da) for differentiation. This segmented approach maintains comprehensive coverage while reducing overall processing time by concentrating detailed analysis on specific discriminatory ranges.
Solution Approach 2:
The system performs preliminary filtering to identify candidate microorganism groups before applying detailed analysis. By pre-identifying relevant candidates and their characteristic molecular weight ranges, the system avoids unnecessary comprehensive analysis of all possible microorganisms, thus reducing processing time while maintaining reliability.
3Measurement precision
If only ribosomal protein spectra are used for identification, then the method is straightforward, but it fails to distinguish between microorganisms with similar ribosomal profiles
Solution Approach 1:
The patent merges ribosomal protein spectrum analysis with additional molecular weight range analysis (500-3000 Da) and peak intensity ratio comparisons. By combining multiple analytical approaches, the system achieves superior species differentiation capability while managing complexity through systematic integration of methods.
Solution Approach 2:
The patent creates a composite identification approach that integrates multiple spectral characteristics (ribosomal profiles, small molecule profiles, peak intensity ratios) into a unified identification framework. This composite methodology enables differentiation of closely related microorganisms that cannot be distinguished by single-method analysis.
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 the accuracy of microbial identification by distinguishing between previously indistinguishable microorganisms, such as Bacillus cereus and Bacillus thuringiensis, optimizing data throughput and ensuring precise results.
Implementation Method 1
time-of-flight (TOF) MS refers to a method using a simple principle among various MS-based methods. In detail, the TOF MS is used to analyze mass of an ion based on an amount of time used for the ion, which is accelerated in an electric field and has a speed, to travel a certain distance to a detector at the speed
Implementation Method 2
the ion, which is accelerated in an electric field and has a speed, to travel a certain distance to a detector
Data Source
AI summary
An apparatus for processing time-of-flight mass spectrometry (TOF MS) data is disclosed. The apparatus may determine at least one candidate microorganism group based on a first molecular weight range in TOF MS data of a sample, determine a second molecular weight range based on a characteristic of the candidate microorganism group in response to a plurality of candidate microorganisms being included in the candidate microorganism group, and identify a microorganism included in the sample from among the candidate microorganisms based on the second molecular weight range in the TOF MS data.