Spectroscopic Calibration Models for Rapid Instrument Adaptation
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Solution Overview
Problem
Conventional spectroscopic instrument calibration requires extensive manual effort, expertise, and is labor-intensive, often necessitating full recalibration for different use cases, leading to instrument downtime and inefficiency.
Innovation Solution
A machine-learning-based approach using a base model trained on multiple instruments and finetuned for specific instruments, reducing the need for extensive calibration data and enabling rapid adaptation to new instruments or use cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional full recalibration is performed for different use cases, then measurement precision is improved, but loss of time and productivity deteriorate due to instrument downtime
Solution Approach 1:
The system performs preliminary actions by training a base calibration model in advance using aggregated calibration data from multiple instruments and use cases. This pre-trained model serves as a foundation that can be quickly adapted to new instruments through finetuning, eliminating the need for time-consuming full recalibration when instruments are deployed for different use cases.
Solution Approach 2:
The base calibration model is trained on aggregated calibration data from multiple spectroscopic instruments across different use cases, creating a universal model that can serve multiple instruments and applications. This multi-functional approach allows the same base model to be adapted to various instruments through finetuning, reducing the need for separate full calibrations for each use case.
2Measurement precision
If conventional manual calibration is performed, then measurement precision is improved, but device complexity and ease of operation worsen due to labor-intensive procedures requiring skilled technicians
Solution Approach 1:
The system enables self-service calibration by automatically performing the calibration process through machine learning model finetuning. The automated workflow eliminates the need for skilled technicians to manually perform calibration procedures, allowing users to quickly adapt instruments to new use cases through automated model training and deployment.
Solution Approach 2:
The patent replaces manual mechanical calibration procedures with an automated computational approach using machine learning. Instead of technicians physically adjusting and calibrating instruments, the system uses automated model finetuning based on calibration data, substituting human labor with computational processes that are both more accurate and easier to execute.
3Manufacturing precision
If extensive calibration data is collected for each instrument, then manufacturing precision is improved, but loss of time and productivity worsen due to data collection requirements
Solution Approach 1:
The system merges calibration data from multiple spectroscopic instruments and use cases into a unified base calibration model. By combining datasets across different instruments and applications, the system creates a comprehensive base model that captures general calibration patterns, reducing the amount of instrument-specific data needed for subsequent finetuning.
Solution Approach 2:
The system applies partial action by performing finetuning on a pre-trained base model rather than training from scratch. This approach uses only the specific calibration data necessary for adapting to a particular instrument or use case, rather than requiring extensive comprehensive calibration data, thereby achieving high precision with reduced data collection time.
Data Source
AI summary
Disclosed herein are scientific instrument support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a method of supporting spectroscopic calibration may include: generating a base calibration model using data from multiple base spectroscopic instruments, and finetuning the base calibration model using data from a target spectroscopic instrument to generate a target calibration model for use with the target spectroscopic instrument. In some embodiments, the number of wavelengths used in generating the base calibration model and/or the target calibration model may be less than the total number of wavelengths represented in the output of the spectroscopic instruments.