Fingerprint Similarity Analysis for Complex Samples
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Solution Overview
Problem
Existing fingerprint similarity analysis techniques for chromatograms are limited by their reliance on retention time as a matching criterion, which is not suitable for complex samples, and they lack automated methods for evaluating mass spectrometry data, leading to time-consuming and inconsistent results.
Innovation Solution
The development of a software solution that employs multiple algorithms for assessing similarity between fingerprint spectra of samples and reference samples, automatically performing qualitative assessments based on chromatographic retention times and mass spectrometry features, and providing automatic generation of new acquisition techniques for further confirmation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If retention time is used as the matching criterion for fingerprint similarity analysis, then the analysis process is simple, but the method is not suitable for complex samples and produces inconsistent results
Solution Approach 1:
The patent segments the fingerprint similarity analysis into multiple independent modules: retention time matching module, mass spectral fingerprint matching module, and comprehensive similarity calculation module. Each module processes specific aspects of the comparison independently, allowing the system to handle complex samples reliably while maintaining manageable process complexity through modular design.
Solution Approach 2:
The patent transitions from one-dimensional retention time matching to multi-dimensional analysis by incorporating mass spectral fingerprint data as an additional dimension. This dimensional expansion enables the system to differentiate compounds that co-elute in chromatography, significantly improving result consistency for complex samples while adding structured complexity to the analysis process.
2Ease of operation
If manual evaluation methods are used for mass spectrometry data, then flexibility is maintained, but the process becomes time-consuming and inconsistent
Solution Approach 1:
The patent implements self-service automation where the system automatically performs mass spectral fingerprint extraction, peak alignment, similarity calculation, and result interpretation without manual intervention. The automated workflow maintains operational flexibility through configurable parameters while dramatically improving analysis efficiency and consistency by eliminating human variability in the evaluation process.
Solution Approach 2:
The patent enables flexible parameter configuration for different analysis scenarios, allowing users to adjust similarity thresholds, mass tolerance values, and chromatographic alignment parameters. This parameter-based control maintains operational flexibility while the automated processing of these parameters ensures consistent, reproducible results across different samples and operators.
3Device complexity
If single algorithm is used for similarity assessment, then the method is simple to implement, but the accuracy and reliability of results are limited
Solution Approach 1:
The patent merges multiple similarity assessment algorithms (cosine similarity, Pearson correlation coefficient, and Euclidean distance) into a comprehensive evaluation framework. Each algorithm calculates similarity from different mathematical perspectives, and their combined results provide a more accurate and reliable assessment of fingerprint similarity than any single algorithm could achieve alone, while maintaining implementation simplicity through standardized calculation procedures.
Solution Approach 2:
The patent creates a universal similarity assessment system that can handle different sample types, chromatographic conditions, and mass spectrometry configurations through multiple algorithms. This multi-functional approach allows the same system to accurately assess similarity across diverse analytical scenarios, improving measurement precision while maintaining algorithmic complexity at a manageable level through unified software implementation.
Data Source
AI summary
In some examples, an apparatus may include analyzing a sample, and analyzing at least one reference sample. A determination may be made as to whether the sample includes a library match score. Based on a determination that the sample includes the library match score, the library match score and a retention time may be analyzed to determine similarity between the sample and the at least one reference sample.