Automated Gas Chromatography Peak Alignment via Deep Learning
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Solution Overview
Problem
Conventional techniques for gas chromatography peak alignment are inefficient and inaccurate due to manual intervention and inadequate training datasets, leading to inconsistent results and limited applicability in portable devices.
Innovation Solution
A deep learning-based method for automated gas chromatography peak alignment, utilizing a trained peak alignment model and post-processing algorithms to generate peak match probabilities and identify volatile organic compounds (VOCs) from chromatographic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional semi-automated peak alignment algorithms (DTW, COW) are used, then peak alignment can be achieved, but manual intervention is required which makes the process slow and inconsistent
Solution Approach 1:
The system employs deep learning models that automatically perform peak alignment without requiring manual parameter tuning or operator intervention. The model self-optimizes by learning from training data, eliminating the need for operators to manually adjust DTW or COW parameters while maintaining high alignment speed and consistency.
Solution Approach 2:
The patent replaces conventional mechanical/mathematical algorithms (DTW, COW) with an intelligent deep learning system. This substitution transforms the alignment process from a rule-based computational method to a data-driven neural network approach, achieving both full automation and high productivity simultaneously.
2Quantity of substance
If conventional machine learning approaches are used with small training datasets, then model training is feasible, but false positives increase due to label imbalances
Solution Approach 1:
The system performs data augmentation and synthetic chromatogram generation before training the deep learning model. By creating additional training samples through simulated chromatograms with known peak alignments, the system prepares a more robust training dataset in advance, reducing false positives and improving reliability even when actual experimental data is limited.
Solution Approach 2:
The patent transforms the training approach by changing from using raw experimental data directly to generating synthetic chromatograms with controlled parameters. This parameter-based generation allows systematic variation of retention times, peak shapes, and co-elution scenarios, creating a more balanced and representative training dataset that improves model reliability.
3Adaptability or versatility
If deep learning models are trained on simple datasets with small numbers of VOCs, then training is manageable, but the models cannot be extended to complex chromatographic data with large numbers of VOCs
Solution Approach 1:
The deep learning model is designed with a universal architecture that can handle varying numbers of VOCs and different chromatographic configurations. By training on diverse synthetic data that includes various VOC combinations and co-elution scenarios, the model becomes adaptable to both simple and complex datasets without requiring separate models or significant architectural changes.
Solution Approach 2:
The system addresses the scalability issue by adding a dimension of synthetic data generation that encompasses a wide range of VOC scenarios. Instead of expanding the model architecture to handle complexity, the solution expands the training data dimension to include diverse VOC combinations, retention time patterns, and co-elution cases, enabling the same model to generalize across different VOC quantities.
4Measurement precision
If conventional techniques requiring two-dimensional data (GC-MS) are used, then meaningful results can be obtained, but sophisticated and large devices are required which reduce portability
Solution Approach 1:
The patent extracts and utilizes only the essential one-dimensional chromatogram data from the GC output, eliminating the need for the mass spectrometry component. By focusing on the retention time and peak shape information from the chromatogram alone, the system achieves VOC identification without requiring the complex and bulky MS instrumentation, thereby enabling portable device implementation while maintaining measurement precision through the deep learning model's ability to interpret chromatographic patterns.
Data Source
AI summary
Systems and methods for aligning gas chromatography peaks are disclosed. An example method includes receiving chromatographic data of a user that includes data representing at least one volatile organic compound (VOC), and analyzing the chromatographic data using a trained peak alignment model to output a set of peak match probabilities. The trained peak alignment model may be trained using a plurality of chromatographic data to output a plurality of peak match probabilities. The example method may further include generating a set of identified VOCs between the chromatographic data and a set of reference VOCs by applying a post-processing algorithm to the set of peak match probabilities; and causing the set of identified VOCs to be displayed to the user.