Local Adaptive Fusion Regression for Spectral Calibration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current chemical analysis methods fail to accurately predict analyte amounts in new samples due to their inability to account for hidden matrix effects, leading to inaccurate calibration and prediction, especially in handheld devices where real-time adaptation is necessary.
Innovation Solution
The Local Adaptive Fusion Regression (LAFR) algorithm uses an indicator of system uniqueness (ISU) and sample-wise differences to identify matrix-matched samples from a library, forming a local training set that accurately predicts target sample analyte amounts by adapting to diverse physicochemical and physiochemical conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current chemical analysis methods use standard calibration approaches, then the process is simple and fast, but prediction accuracy deteriorates due to inability to account for hidden matrix effects
Solution Approach 1:
The patent segments the calibration process into multiple local models instead of using a single global model. Each local model is trained on a specific subset of calibration samples that share similar matrix effects, allowing the system to capture local variations in the data while maintaining overall predictive accuracy across diverse sample types.
Solution Approach 2:
The patent implements a dynamic model selection mechanism that automatically chooses the most appropriate local model for each prediction based on the characteristics of the query sample. This dynamic adaptation allows the system to respond to varying matrix effects in real-time, improving prediction accuracy without requiring manual intervention or complex fixed-structure models.
2Measurement precision
If LAFR forms multiple local training sets to account for matrix effects, then prediction accuracy improves, but computational time and complexity increase
Solution Approach 1:
The patent performs preliminary clustering of calibration samples into groups with similar matrix effects during the offline training phase. This pre-organization of data allows the system to quickly select and apply the appropriate local model during online prediction, avoiding the need to process all calibration samples each time a prediction is made, thus reducing computational time while maintaining accuracy.
Solution Approach 2:
The patent applies local quality by training separate specialized models for different regions of the calibration space rather than using a single homogeneous model. Each local model is optimized for its specific region, providing higher prediction accuracy for samples in that region while the overall system remains efficient through intelligent model selection based on sample characteristics.
3Adaptability or versatility
If the calibration model accounts for diverse physicochemical conditions, then adaptability improves, but model complexity and difficulty of implementation increase
Solution Approach 1:
The patent segments the diverse calibration space into multiple manageable local regions, each characterized by similar physicochemical conditions. This segmentation allows the system to handle complex variability by breaking it down into simpler local patterns that can be captured by individual local models, making the overall system more adaptable without requiring an impossibly complex single model.
Solution Approach 2:
The patent implements self-service through automatic model selection and application. The system autonomously determines which local model is most appropriate for each prediction based on the query sample's characteristics, eliminating the need for manual model selection or complex user configuration. This self-service mechanism simplifies implementation while maintaining high adaptability to diverse conditions.
Data Source
AI summary
Methodologies and corresponding systems for a Local adaptive fusion regression (LAFR) process are able to search a large library of spectral measurement for a linear calibration (training) set, which is spectrally matrix matched to a target sample spectrum, and also tightly bracketed about an “unknown” prediction property (analyte) for the target sample. Using a matched calibration set, the likelihood of an accurate prediction by the selected calibration set is greatly enhanced. The LAFR process integrates multiple spectral similarity information with contextual considerations between source analyte contents, model, and analyte predictions. LAFR facilitates onsite chemical analysis such as with a handheld spectrometer, dedicated in-line process analyzers and benchtop instruments. LAFR is based on a Beer’s law like linear relationship where a calibration model (mathematical relationship) is made that linearly relates the analyte amount, e.g., concentration, to the measured spectral responses. The calibration model is then used to predict (quantitate) the analyte amounts present in new samples.