Summed Ion Spectra for Forensic Substance Classification
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Solution Overview
Problem
Existing methods are inadequate for classifying components in complex mixtures, particularly in identifying the class of ignitable liquids or explosive materials in forensic analysis, as they are not designed to handle component classifications effectively.
Innovation Solution
A system and method utilizing ion intensity quantification, principal components analysis, target factor analysis, and Bayesian decision theory to identify classes of substances by generating summed ion spectra and comparing them with reference libraries, reducing data dimensionality and determining correlations to classify components accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing identification methods are used to analyze complex mixtures, then individual chemical compounds can be identified through separation, but component classification (e.g., ignitable liquid classes) cannot be effectively determined
Solution Approach 1:
The patent segments the complex mixture analysis into distinct analytical stages: (1) separation of individual chemical compounds, (2) generation of summed ion spectra for each compound, and (3) classification of compounds into components based on spectral patterns. This segmentation enables the system to handle complex mixtures by breaking down the classification problem into manageable steps, ultimately achieving component classification accuracy that existing methods cannot provide.
Solution Approach 2:
The patent transitions from traditional one-dimensional chemical compound identification to multi-dimensional analysis by generating summed ion spectra that capture relationships among multiple chemicals. This dimensional expansion allows the system to classify compounds into components (e.g., ignitable liquid classes) based on spectral patterns, providing versatility for analyzing various complex mixture types including fire debris and explosive materials.
2Adaptability or versatility
If separation methods are used to identify individual chemicals, then compound identification is achieved, but classification of chemical combinations into components (e.g., ignitable liquids) is not possible
Solution Approach 1:
The patent merges individual chemical compound data into summed ion spectra that represent entire components (e.g., ignitable liquids). By combining spectral information from multiple chemicals that belong to the same component, the system achieves reliable component classification. This merging process enables the system to identify not just individual compounds but also their groupings into meaningful components with high classification reliability.
Solution Approach 2:
The patent creates reference copies of summed ion spectra for known components and stores them in a database. These reference spectra serve as templates for comparing against unknown samples, enabling reliable component classification through pattern matching. This copying approach allows the system to accurately identify component classes by comparing spectral patterns without requiring direct physical reference samples during analysis.
3Reliability
If traditional analysis methods are applied to fire debris samples, then individual chemicals can be detected, but the class of ignitable liquid present cannot be determined
Solution Approach 1:
The patent introduces summed ion spectra as an intermediary representation between individual chemical detection and ignitable liquid class identification. This intermediary layer processes and integrates information from multiple chemicals, enabling reliable classification of ignitable liquid classes. The intermediary spectra serve as a bridge that translates complex chemical mixture data into meaningful component classifications, managing system complexity while achieving high identification reliability.
Solution Approach 2:
The patent replaces traditional mechanical separation and identification methods with a spectral analysis system that uses ion intensity measurements and pattern recognition. This substitution eliminates the need for complex physical separation procedures while achieving more reliable ignitable liquid class identification through computational analysis of summed ion spectra, thereby managing device complexity while improving reliability.
Data Source
AI summary
In one embodiment, a system and a method for identifying the class of a component of a mixture includes collecting samples from a sample source, determining a summed ion spectrum for each sample and generating sample data from the summed ion spectra, comparing the sample data with reference summed ion spectra of multiple reference substances to determine correlations between the reference substances and the sample data, and evaluating the correlations of the substances of each substance class to determine which substance class most closely correlates to the sample data.