Microbial Classification via Mass Spectrum Binning
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Solution Overview
Problem
Current mass spectrometry methods for microbial classification are complex and unreliable, particularly for strain-level identification, which is crucial for determining drug resistance in infectious agents, and often require extensive processing that can reduce accuracy.
Innovation Solution
The use of binned mass spectra, where mass-to-charge ratios are partitioned into bins to generate a binned mass spectrum, allowing for classification without extensive processing, such as deisotoping, and employing a classification algorithm, like an artificial neural network, to determine microbial classification based on the binned spectra.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive processing operations (peak picking, deisotoping, deconvolution, smoothing, cropping) are applied to mass spectra to identify microbial fingerprints, then the completeness of spectral analysis is improved, but the system complexity and processing time increase significantly
Solution Approach 1:
The patent extracts and eliminates unnecessary processing steps (deisotoping, deconvolution, smoothing, cropping) from the traditional mass spectral analysis workflow. By removing these intermediate processing operations, the system achieves microbial classification using only peak picking and binning, thereby reducing complexity while maintaining or improving accuracy.
Solution Approach 2:
Instead of applying multiple sequential processing operations to refine the mass spectrum before classification, the patent inverts the approach by using raw or minimally processed spectra directly as input to the classification algorithm. This inversion eliminates the need for complex intermediate processing steps.
2Loss of information
If traditional extensive processing methods are used for mass spectrum analysis, then comprehensive spectral features are captured, but the reliability of strain-level identification decreases
Solution Approach 1:
The patent removes deisotoping and deconvolution operations from the processing pipeline, which were found to eliminate or distort strain-specific spectral information. By taking out these operations, the system preserves complete spectral information including isotopic patterns and multiply-charged species that are critical for reliable strain-level identification.
Solution Approach 2:
The patent uses binning to create a simplified representation of the mass spectrum that preserves all essential spectral features without the information loss caused by traditional processing. The binning approach copies and aggregates spectral data in a way that maintains strain-specific information while reducing noise and variability.
3Object-affected harmful factors
If multiple processing operations are performed on mass spectra, then spectral noise is reduced through smoothing, but the processing time and computational resources increase
Solution Approach 1:
The patent eliminates time-consuming processing operations such as deisotoping, deconvolution, and cropping from the workflow. By removing these operations, the system achieves rapid processing of mass spectral data while still effectively managing spectral noise through peak picking and binning operations alone.
Solution Approach 2:
The patent skips intermediate processing steps that traditionally slow down analysis. By rushing through to classification using only essential operations (peak picking and binning), the system achieves faster processing speeds without sacrificing the ability to handle spectral noise effectively.
Data Source
AI summary
Techniques for determining a microbial classification based on a mass spectrum are disclosed. A mass spectrometer generates a mass spectrum for a biological sample. A binning function is applied to the mass spectrum to generate a binned mass spectrum. As an example, a binned mass spectrum is associated with a set of bins having mass errors of the same value. A classification algorithm is applied to the binned mass spectrum to determine a microbial classification.