Spectral Quantification Validation Using Secondary Output Predictions
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Solution Overview
Problem
Existing spectroscopy techniques, such as LIBS, struggle to validate the reliability of concentration predictions due to uncontrolled variables and high relative uncertainties near detection limits, lacking methods to verify if training standards represent the measured samples and assess prediction confidence.
Innovation Solution
A multi-variate, multi-output quantification model using deep-learning architectures that predict both species concentrations and verifiable secondary outputs, such as spectral line intensities, to determine confidence in predictions through measurable discrepancies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional quantification models are used to predict species concentrations from spectral data, then concentration predictions can be obtained, but the reliability and confidence of these predictions cannot be validated
Solution Approach 1:
The model output is segmented into two distinct components: primary predictions for species concentrations and secondary predictions for spectral line intensities. This segmentation allows independent validation of the concentration predictions by comparing secondary predictions against actual measured spectral data, thereby resolving the reliability issue while maintaining concentration prediction accuracy
Solution Approach 2:
The invention implements a feedback mechanism where secondary predictions of spectral line intensities are compared with actual measured spectral data to compute a confidence metric. This feedback loop validates the primary concentration predictions by checking consistency between predicted and observed spectral features, enabling reliability assessment without compromising concentration measurement precision
2Reliability
If multi-output models predict both concentrations and spectral line intensities, then prediction reliability can be validated through secondary outputs, but the model complexity increases
Solution Approach 1:
The multi-output model serves multiple functions simultaneously: it predicts species concentrations for quantitative analysis and predicts spectral line intensities for validation purposes. This multi-functionality enables reliability validation without requiring separate models, as the same neural network architecture performs both prediction and self-validation tasks, thereby managing complexity while improving reliability
3Productivity
If calibration is performed using limited standards, then the model can be trained quickly, but the representativeness of standards for unknown samples cannot be verified
Solution Approach 1:
The model performs preliminary validation by predicting spectral line intensities for unknown samples and comparing these predictions with actual measured spectral data before finalizing concentration predictions. This preliminary check verifies whether the sample falls within the calibration range without requiring additional calibration standards, thus maintaining fast calibration speed while ensuring standard representativeness through automated consistency checking
Data Source
AI summary
A computer-implemented machine-learning method for learning a multi-output prediction model is configured to jointly determine, based on a set of characteristic spectral data of a sample, at least one primary prediction of at least one first physical quantity characterizing a given species in the sample and at least one secondary prediction of at least one second physical quantity characterizing the species, the multi-output prediction model being trained using a set of annotated spectral data.