Gas Sample Analysis Using FTIR Regression and Least Squares Minimization
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Solution Overview
Problem
Gas Chromatography-Mass Spectrometry (GC-MS) systems face limitations such as compound separation interference, non-linear calibrations, poor precision and accuracy, and limited dynamic range, requiring constant calibration and inability to perform quantitative analysis on unidentified peaks.
Innovation Solution
A system and method using a combinatorial module that generates solutions by performing regression analysis and minimizing least squares errors between computed and sample spectra, selecting candidate species based on retention indices and spectral information, and iteratively combining species to identify and quantify components in a sample.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If GC-MS is used for analyzing gas samples, then compound separation and identification capability is improved, but measurement precision deteriorates due to non-linear calibrations and poor precision requiring constant calibration
Solution Approach 1:
The patent transforms the analysis from mass spectrometry-based quantitative measurement to FTIR spectroscopy-based identification, changing the fundamental measurement parameter from mass-to-charge ratio to infrared absorption spectrum. This parameter change eliminates the need for complex calibration curves and non-linear corrections, achieving both versatility in compound identification and improved measurement precision through direct spectral matching.
2Device complexity
If GC-MS requires user selection of compound lists prior to analysis, then device complexity is reduced by focusing on specific compounds, but productivity deteriorates due to inability to perform quantitative analysis on unidentified peaks
Solution Approach 1:
The FTIR spectroscopy system performs self-service analysis by automatically identifying and quantifying all detectable compounds in the sample without requiring pre-selection of compound lists. The system uses pattern recognition algorithms to automatically match spectral fingerprints of unknown compounds against reference libraries, enabling both targeted analysis of expected compounds and unexpected peak identification, thereby improving productivity while maintaining manageable complexity.
3Speed
If GC-MS is used for rapid analysis, then speed of analysis is improved, but loss of information increases due to inability to identify and quantify unknown peaks
Solution Approach 1:
The FTIR spectroscopy system provides universal analysis capability that simultaneously handles both known and unknown compounds through a single unified approach. The system's spectral fingerprinting method can identify any compound present in the sample regardless of whether it was pre-selected or expected, enabling rapid analysis without loss of information about unknown peaks while maintaining the speed advantage of FTIR's fast acquisition capability.
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 rapid and accurate identification and quantification of molecular species in a sample, improving precision and reducing analysis time by filtering candidate species and using peak matching to refine combinations, thus overcoming the limitations of GC-MS systems.
Implementation Method 1
Gas Chromatography (GC) is used to resolve a mixture into its various components according to retention profiles of the different molecules passing through a GC column
Implementation Method 2
GC has been integrated with techniques such as mass spectrometry (MS) or Fourier transform infrared (FTIR) spectrometry
Implementation Method 3
GC has been integrated with techniques such as mass spectrometry (MS) or Fourier transform infrared (FTIR) spectrometry
Implementation Method 4
the combinatoric module generates the solution by performing a regression analysis and minimizing least squares errors between a computed spectrum and a sample spectrum
Data Source
AI summary
A method is provided for analyzing a sample and identifying species using chromatography and spectrometry. Possible candidate species to be used in a regression analysis are selected for consideration based on their retention indices in a chromatography column and peak locations in an infrared spectrum. By using such a selection process, the number of combinations of species to be used in the regression analysis can be significantly reduced. The species and respective concentrations in the sample are identified by using an iterative process with regression analysis and minimizing least squares errors between a sample spectrum and a computed spectrum associated with selected candidate species.