Hierarchical Mass Spectrum Grouping for Microbe Identification
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Solution Overview
Problem
Current mass spectrometry methods for identifying microorganisms face challenges in accurately categorizing samples due to variability in experimental conditions and spectral data, often averaging out or eliminating potentially diagnostic mass peaks when consolidating reference spectra.
Innovation Solution
A computer-implemented method that compares a sample mass spectrum with reference spectra, assigns similarity indexes, and combines them hierarchically to provide group indexes for spectra sharing common characteristics, allowing for more precise identification without needing to consider each individual similarity index.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple reference spectra are consolidated into a single reference spectrum to capture variability, then the library comprehensiveness is improved, but diagnostic mass peaks may be averaged out or eliminated
Solution Approach 1:
The patent segments the reference spectra library into multiple individual reference spectra rather than consolidating them into a single averaged spectrum. Each reference spectrum is kept separate and intact, allowing the system to evaluate multiple possible references against the sample spectrum. This segmentation preserves diagnostic mass peaks in each individual reference while still providing comprehensive coverage of variability through the collection of multiple references.
2Adaptability or versatility
If an excessive number of reference spectra are stored in the library to capture all variability, then the adaptability is improved, but the complexity of analysis and processing increases
Solution Approach 1:
The patent replaces complex manual analysis and evaluation processes with automated computational methods. The control system automatically compares the sample spectrum against multiple reference spectra, calculates match scores, and identifies the best match without requiring manual intervention. This substitution of mechanical/manual processes with automated computational algorithms reduces analysis complexity while maintaining comprehensive coverage of variability through the use of multiple reference spectra.
3Measurement precision
If individual reference spectra are used without consolidation, then diagnostic mass peaks are preserved, but the ability to account for experimental variability is reduced
Solution Approach 1:
The patent merges multiple individual reference spectra into a unified library that collectively represents the full range of experimental variability. By storing and evaluating multiple reference spectra side-by-side, the system combines the strengths of individual spectra (preserved diagnostic peaks) with the benefits of collective representation (coverage of variability). The control system evaluates the sample spectrum against all references and selects the best match, thereby accounting for variability while preserving diagnostic information.
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 enables accurate and rapid categorization of samples by addressing variability in mass spectra, allowing for both precise and less precise identifications at different levels of hierarchy, improving upon conventional methods by combining similarity indexes to represent the probability of a sample belonging to a specific group.
Implementation Method 1
A sample mass spectrum of the sample microbe can be obtained by Matrix Assisted Laser Desorption lonisation ('MALDI') Time of Flight ('ToF') mass analysis. A matrix substance is added to the sample plate which is used in a subsequent ionization by a Matrix Assisted Laser Desorption lonisation ion source to generate positively charged ions.
Implementation Method 2
The generated ions are then mass analysed using a Time of Flight mass analyser.
Data Source
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AI summary
A method of analysing a sample mass spectrum comprises comparing a sample mass spectrum of a sample with each reference mass spectrum of plural reference mass spectra. A similarity index is assigned to each reference mass spectrum of the plural reference mass spectra based on similarity between the sample mass spectrum and the reference mass spectrum. For each group of one or more groups of the plural reference mass spectra, the similarity indexes for the reference mass spectra belonging to the group are combined so as to provide a group index for the group at a first level of a hierarchy of sample characteristics. The reference mass spectra belonging to each group are mass spectra of reference samples that have a particular characteristic in common. The method provides a way to categorise a sample as belonging to a particular group of reference samples.