Chromatographic Signal Deconvolution via Dirichlet Process
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Solution Overview
Problem
Current analytical techniques struggle to accurately identify and quantify chemical constituents in biopharmaceutical purification processes due to poor resolution, leading to subjective assumptions and systematic errors, especially when dealing with overlapping or convoluted chromatographic signals.
Innovation Solution
The development of signal processing systems that convert chromatographic time-series composite signals into a count distribution using algorithms like the Dirichlet process, allowing for robust deconvolution of overlapping peaks and objective identification of chemical constituents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional chromatographic methods are used to separate and quantify chemical constituents, then the analysis can be performed with standard equipment, but the resolution is poor leading to overlapping peaks and inaccurate quantification
Solution Approach 1:
The patent introduces an intermediary computational layer (probability distribution realization algorithm) between the raw chromatographic signal and the final quantification results. This algorithm acts as a mediator that processes the overlapping peaks mathematically, converting the convoluted signal into distinct probability distributions for each chemical constituent, thereby achieving accurate quantification despite poor chromatographic resolution
Solution Approach 2:
The patent replaces the mechanical/physical separation system (chromatography) with a computational/mathematical system (probability distribution algorithms). Instead of relying on physical peak separation through optimized chromatographic conditions, the invention uses mathematical deconvolution to resolve overlapping signals, substituting mechanical resolution with computational analysis
2Adaptability or versatility
If the number of chemical constituents is unknown, then the analysis can be applied to diverse samples, but subjective assumptions about peak composition lead to systematic errors
Solution Approach 1:
The patent enables the system to determine the number of chemical constituents autonomously through the probability distribution realization algorithm. The algorithm automatically identifies the optimal number of components by analyzing the statistical properties of the signal, eliminating the need for analyst subjectivity while maintaining versatility across different sample types with unknown compositions
Solution Approach 2:
The patent implements a feedback mechanism where the algorithm iteratively refines the number of chemical constituents and their parameters by comparing the reconstructed signal with the observed chromatographic data. This feedback loop ensures reliable and consistent identification of constituents across diverse samples without requiring prior knowledge or subjective assumptions
3Productivity
If flow rate is increased to accelerate the assay, then the analysis speed improves, but chromatographic resolution decreases causing greater peak overlap
Solution Approach 1:
The patent converts the harmful effect of peak overlap (caused by high flow rates) into a beneficial opportunity for computational analysis. By deliberately operating at high flow rates to achieve fast analysis, the system generates convoluted signals that are then resolved through probability distribution algorithms, transforming the disadvantage of poor resolution into a demonstration of the algorithm's deconvolution capability
Data Source
AI summary
The present disclosure is directed to systems and methods for accessing and analyzing a chromatographic time-series composite signals comprising multiple signal distributions and deconvoluting the signal distributions to correlate such deconvoluted signals with chemical constituents in a variety of sample types, e.g., biopharmaceutical purification process samples.