Neural Network Training Data Generation for NMR Signal Identification
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Solution Overview
Problem
Current methods for identifying signal intervals in NMR spectra are subjective, error-prone, and rely heavily on human expertise, making them inefficient and prone to errors.
Innovation Solution
A computer-implemented method generates a realistic training data set for a neural network to automatically identify signal intervals in NMR spectra by simulating spectra with varying line widths and adding perturbations like impurities, phase shifts, and noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If human experts manually identify signal intervals in NMR spectra, then the analysis can be performed with current technology, but the process is subjective, error-prone, and inefficient
Solution Approach 1:
The patent creates synthetic NMR spectra that copy the characteristics of real spectra by incorporating line broadening, baseline variations, noise, and impurity signals. These synthetic spectra serve as training data that replicates the complexity of real-world measurements, enabling the neural network to learn accurate signal interval identification without requiring manual annotation of real spectra by experts.
Solution Approach 2:
The patent systematically varies multiple parameters including line width, baseline offset, noise level, and impurity concentration to generate diverse training data. This parameter variation ensures the neural network learns robust features that generalize across different experimental conditions, improving both automation capability and reliability of signal identification.
2Measurement precision
If multiple FIDs are acquired and averaged to improve signal-to-noise ratio, then the signal quality improves, but the measurement time increases
Solution Approach 1:
The patent replaces the mechanical process of acquiring multiple FIDs and performing manual spectral analysis with a neural network system that processes single spectra using learned patterns from synthetic training data. The neural network directly identifies signal intervals in the frequency domain without requiring time-domain signal accumulation, thereby maintaining high measurement precision while significantly reducing measurement time.
3Extent of automation
If deep learning models are trained on simulated spectrum data, then automated analysis can be achieved, but the models struggle with real-world spectral variations
Solution Approach 1:
The patent generates synthetic training spectra by copying real spectral characteristics including line broadening, baseline drift, noise patterns, and impurity interference. These synthetic copies faithfully replicate real-world variations, enabling the neural network to learn robust features that transfer effectively to actual NMR measurements without requiring manual annotation of real spectra.
Solution Approach 2:
The patent performs preliminary training on extensively varied synthetic data that pre-exposes the neural network to a wide range of spectral distortions and conditions. This preliminary action builds robust feature representations before the model encounters real-world data, ensuring high adaptability and versatility across different experimental scenarios.
Data Source
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AI summary
A system (100), method and computer program product for training a neural network (230) for signal analysis in NMR spectra. The system obtains a plurality of computed NMR raw spectra (213), each raw spectrum being associated with a different NMR active molecule (211) having a known number of protons (#P). The system broadens peaks of the raw spectra by convolution of each raw spectrum (213) with one or more line shaping functions to generate a broadened spectrum (111) for each raw spectrum. The broadening of line widths follows a statistical distribution over the plurality of current spectra by sampling broadening values for the various raw spectra from the statistical distribution. The system computes for each broadened spectrum (111) its integral function to count the number of protons associated with peaks of the respective broadened spectrum. The system identifies signal intervals as intervals in the broadened spectrum (111) where the integral function increases approximately by multiples of the value associated with a single proton so that the total number of counted protons matches the known number of protons (#P) of the associated molecule. The identified intervals (211) are adjusted to cover at least a predefined threshold value of corresponding peak integrals. The obtained spectra (111, 111') with associated labels (121) for the identified signal intervals are provided as the training data set (141) to the neural network.