Microorganism Discrimination Using Wavelet-Masked Mass Spectra
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Solution Overview
Problem
Conventional MALDI-MS methods face challenges in accurately discriminating between closely related microorganisms, such as subspecies, strains, or types, requiring the analysis of numerous biomarker peaks.
Innovation Solution
A method and system that acquire mass spectra from known microorganisms, create a mask based on marker-candidate protein m/z values, perform continuous wavelet transforms, and use machine learning to create a discriminant model for accurate discrimination of unknown microorganisms by transforming their mass spectra into wavelet images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MALDI-MS methods are used to discriminate closely related microorganisms, then discrimination can be performed, but a considerable number of biomarker peaks must be checked, reducing efficiency
Solution Approach 1:
The patent extracts only the essential biomarker peaks that are actually useful for discrimination from the complete mass spectrum. By identifying and isolating these key peaks through the proposed method, the system eliminates the need to analyze all peaks, thereby maintaining high discrimination accuracy while significantly improving processing efficiency.
Solution Approach 2:
The patent applies local quality by assigning different importance weights to different regions of the mass spectrum. Instead of treating all peaks equally, the method identifies specific local regions containing biomarker peaks and focuses analysis there, allowing efficient discrimination with fewer peaks while maintaining accuracy.
2Measurement precision
If more biomarker peaks are checked to improve discrimination accuracy, then discrimination precision improves, but the complexity and time required increases
Solution Approach 1:
The patent performs preliminary action by pre-identifying and marking the positions of biomarker peaks before the actual discrimination analysis. This preliminary step allows the system to quickly locate and focus only on relevant peaks during analysis, reducing the time required while maintaining high precision discrimination.
Solution Approach 2:
The patent applies partial action by analyzing only the necessary subset of biomarker peaks rather than all possible peaks. This selective approach achieves sufficient discrimination precision with reduced analysis time, avoiding the excessive action of checking every possible peak.
3Ease of operation
If traditional peak comparison methods are used, then the process is straightforward, but discrimination accuracy for closely related microorganisms is insufficient
Solution Approach 1:
The patent changes the parameters used for discrimination by introducing a scoring system that evaluates multiple characteristics of biomarker peaks (such as position, intensity, and pattern). This parameter transformation allows the method to maintain simplicity while achieving high discrimination accuracy for closely related microorganisms that traditional methods cannot distinguish.
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 highly accurate and efficient discrimination of microorganisms by enhancing the visibility of differences between subspecies, strains, or types, allowing for improved classification capabilities using high-performance machine-learning algorithms.
Implementation Method 1
matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS)
Implementation Method 2
matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS)
Data Source
AI summary
To enable a correct and easy discrimination of microorganisms, a microorganism discrimination method includes: acquiring mass spectra related to known microorganisms which belong to the same species and whose subspecies, strains or types are known (S11); retrieving a list describing m/z values of marker-candidate proteins which are supposed to vary in mass among different subspecies, strains or types (S12); creating a mask which gives non-zero values only within a predetermined range including each of the listed m/z values (S14); masking each of the mass spectra (S15); creating wavelet images by performing continuous wavelet transform on the mass spectra (S16); creating a discriminant model by machine learning using, as training data, the wavelet images and information of the subspecies, strains or types of the known microorganisms; and discriminating the subspecies, strain or type of an unknown microorganism by applying a mass spectrum of this microorganism to the discriminant model.