Spectroscopic Calibration Using Reference Data Matching
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Solution Overview
Problem
Existing methods for developing spectroscopic applications are tedious, time-consuming, and expensive, and pre-existing training sets often fail to generalize well to target populations due to distribution mismatches and instrument deviations, limiting accessibility and updateability.
Innovation Solution
An on-demand method for generating spectroscopic applications using database searches and machine learning to create predictive models based on few samples from the target population, allowing for automatic adjustment and update of spectroscopic devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If pre-existing training sets are used to create spectroscopic applications, then development time and cost are reduced, but the predictive models fail to generalize well to target populations due to distribution mismatches and instrument deviations
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing spectral data from multiple sources (different instruments, populations, conditions) into a comprehensive reference database before actual calibration is needed. This advance preparation enables rapid matching and adaptation when calibration is required, reducing development time while maintaining accuracy through the use of pre-validated reference data
Solution Approach 2:
The patent introduces an intermediary component - a reference database containing spectral data from diverse sources that acts as a mediator between pre-existing training sets and target populations. This intermediary enables the system to bridge distribution mismatches by providing intermediate reference data that connects general pre-trained models to specific target applications, improving generalization without requiring extensive retraining
2Measurement precision
If comprehensive training sets are built from scratch to ensure representative samples, then predictive model accuracy is improved, but the process becomes tedious, time-consuming, and expensive
Solution Approach 1:
The system creates a universal reference database that serves multiple functions and populations simultaneously. Instead of building separate training sets for each application, the database contains spectral data from diverse sources that can be matched and adapted to various target populations, making the calibration process efficient while maintaining accuracy through selective data matching
Solution Approach 2:
The patent employs copying by creating reference spectral profiles from comprehensive sources and then matching/copying relevant patterns to specific target applications. Rather than collecting new data from scratch for each application, the system copies and adapts pre-validated spectral patterns from the reference database, dramatically reducing development time while preserving predictive accuracy
3Reliability
If spectroscopic applications are customized for specific target populations, then predictive performance is optimized, but the complexity and effort of creating and updating models increases
Solution Approach 1:
The system implements dynamic calibration where the reference database and matching algorithms can adapt to different target populations automatically. The system dynamically selects and weights reference data based on similarity matching, enabling customization for specific populations without manual model rebuilding. This dynamic approach maintains optimized predictive performance while reducing complexity through automated adaptation
Solution Approach 2:
The calibration system performs self-service by automatically matching target population characteristics with relevant reference data from the database. The system autonomously identifies distribution mismatches, selects appropriate reference samples, and adjusts predictive models without requiring extensive manual intervention, thereby optimizing performance for specific populations while minimizing customization complexity
Data Source
AI summary
In a method for providing a predictive model for spectroscopy, a response value indicating a physical and/or chemical property of a product or material is predicted from one or more spectral values indicating physical properties of the product or material. For this, spectral measurements of samples of the product are obtained, the spectral measurement including spectral values. Then the spectral values are searched in a database, resulting in data that maximizes the accuracy of the predictive model for spectroscopy of other samples of the product or material. Further a method calibrates a spectroscopic device, a spectroscopic device has installed therein a method for providing a predictive model for spectroscopy, and a computer program product includes program for a processing device including software code portions for performing a method for providing a predictive model for spectroscopy.