Chromatographic Elution Order Prediction via Mathematical Programming
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Solution Overview
Problem
QSRR models are ineffective in accurately predicting the elution order of compounds in complex mixtures, particularly in reversed-phase liquid chromatography, due to the complexity of the RP-LC mechanism, resulting in poor prediction accuracy for hundreds or thousands of close or overlapping peaks.
Innovation Solution
A method using mathematical programming, specifically non-linear programming and multi-objective optimization, to predict chromatographic elution order by constraining the predicted elution order and using retention time and elution order prediction errors as objective functions, with QSRR models represented by linear or non-linear equations such as artificial neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If QSRR models are used to predict retention time of compounds, then prediction capability is provided, but prediction accuracy deteriorates for complex mixtures in reversed-phase liquid chromatography
Solution Approach 1:
The patent transforms the QSRR prediction approach by changing the parameter being optimized from retention time alone to elution order. By using mathematical programming to optimize the sequence of elution based on QSRR predictions, the system achieves accurate separation predictions for complex mixtures in RP-LC without requiring the QSRR model to perfectly predict absolute retention times.
Solution Approach 2:
The patent replaces the direct physical interpretation of retention time predictions with a mathematical optimization framework. Instead of relying on the physical accuracy of retention time predictions, the system uses mathematical programming to determine the optimal elution sequence based on relative retention time relationships, substituting physical prediction accuracy with mathematical optimization.
2Measurement precision
If QSRR models predict retention time for simple mixtures, then reasonable accuracy is achieved, but prediction accuracy deteriorates when hundreds or thousands of compounds are analyzed
Solution Approach 1:
The patent segments the prediction task from predicting absolute retention times for all compounds to predicting the relative elution order. By dividing the problem into determining the sequence of elution rather than precise timing for each compound, the system can handle hundreds or thousands of compounds without the accuracy degradation that occurs when predicting individual retention times for large numbers of substances.
Solution Approach 2:
The patent transitions from a one-dimensional prediction (retention time values) to a two-dimensional prediction (retention time plus elution order). By adding the elution order dimension and using mathematical programming to optimize this sequence, the system achieves accurate predictions for complex mixtures with hundreds or thousands of compounds, overcoming the limitations of traditional QSRR approaches.
Data Source
AI summary
Disclosed is a method for predicting an elution order of compounds in a mixture. The method includes (a) building a quantitative structure-retention relationship (QSRR) model and (b) predicting a chromatographic elution order of the compounds in the mixture on the basis of the QSRR model using mathematical programming. The mathematical programming is a non-linear programming technique in which a predicted elution order of the compounds is used as a constraint or a multi-objective optimization (MOO) in which a retention time prediction error and an elution order prediction error are used as objective functions. With the use of the method of the present disclosure, it is possible to optimize separation of complex mixtures in reversed-phase chromatography by enabling identification of accurate positions of individual compounds that provides higher certainty in identifying a given compound, e.g., during an “omics” analysis (proteomics, metabolomics, etc.).