Wavelet-Based Spectral Deconvolution for Chemical Analysis
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Solution Overview
Problem
Current methods for analyzing chemical spectra in exploratory analysis fail to effectively deconvolute mixture distributions of peaks into individual components, making it difficult to model chemical changes independently in data from chromatography and NMR spectroscopy.
Innovation Solution
A computer-readable medium with instructions that, when executed, provides interactive model selection by fitting wavelet functions to explanatory and response variable vectors, allowing for the identification of individual peaks and their coefficients, which are then used to train models and present results interactively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wavelet functions are fitted to deconvolute spectra into individual peaks, then measurement precision of chemical composition is improved, but device complexity of the analysis system increases
Solution Approach 1:
The patent applies segmentation by deconvoluting complex chemical spectra into individual peak components using wavelet functions. Each peak represents a distinct chemical feature, allowing the system to analyze and model chemical changes at the component level rather than treating the spectrum as a whole, thereby improving measurement precision
Solution Approach 2:
The patent introduces wavelet functions as an intermediary mathematical tool to bridge the gap between raw spectral data and meaningful chemical composition information. These wavelet functions serve as mediators that transform the complex spectrum into interpretable peak components, enabling precise chemical analysis without requiring direct physical separation of components
2Productivity
If models are trained using all spectral data points, then productivity of analysis is improved, but reliability of model accuracy deteriorates due to overfitting
Solution Approach 1:
The patent extracts only the essential peak parameters (position, intensity, shape) from the deconvoluted spectral peaks to use as input features for model training. This extraction process removes redundant and noisy data points while retaining the critical chemical information needed for accurate modeling, preventing overfitting while maintaining analysis productivity
Solution Approach 2:
The patent transforms the raw spectral data into a different parameter space by fitting wavelet functions and extracting peak characteristics. This parameter transformation converts continuous spectral data into discrete peak parameters, changing the data representation to be more suitable for chemical modeling and reducing the risk of overfitting
Data Source
AI summary
Graphical interactive model selection is provided. A response variable vector for each value of a group variable and an explanatory variable vector are defined. A wavelet function is fit to the explanatory variable vector paired with the response variable vector defined for each value of the group variable. Each fit wavelet function defines coefficients for each value of the group variable. A curve is presented for each value of the group variable and is defined by the plurality of coefficients of an associated fit wavelet function. An indicator is received of a request to perform functional analysis using the coefficients for each value of the of the group variable based on a predefined factor variable. A model is trained using the coefficients for each value of the group variable and a factor variable value associated with each observation vector of each plurality of observation vectors as a model effect.