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Instrument Calibration

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Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
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Updated: Jun 28, 2026

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

Decision-theoretical formulation of the calibration problem.

M Kárný¹, K M Hangos

¹Institute of Information Theory and Automation Czechoslovak Academy of Sciences Pod vodárenskou vezí 4 Prague 8 18208 Czech Republic.

The Journal of Automatic Chemistry

|January 1, 1989

Summary

This study presents a Bayesian decision theory approach for optimizing calibration policies in analytical chemistry. It offers a framework for improving the accuracy of estimating unknown samples using dynamic programming.

Related Experiment Videos

Last Updated: Jun 28, 2026

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

Area of Science:

Analytical Chemistry
Decision Theory
Mathematical Modeling

Background:

Calibration is crucial in analytical chemistry for accurate sample analysis.
Existing methods may lack a systematic approach to optimize calibration strategies.
Uncertainty in data requires robust methods for reliable results.

Purpose of the Study:

To formulate the practical calibration problem as a mathematical decision task.
To present a Bayesian solution for determining the optimum feedback calibration policy.
To provide a systematic guideline for applying decision theory in analytical chemistry.

Main Methods:

Formulation of the calibration problem as a mathematical decision task.
Application of Bayesian inference for parameter estimation and filtering.
Utilization of dynamic programming to find the optimum feedback calibration policy.
Updating conditional probability distributions for information processing.

Main Results:

A prototype for practical calibration problems is developed.
An optimum feedback calibration policy is derived using dynamic programming.
The importance of probability distributions of unknown samples is highlighted.
An ideal calibration solution is provided for comparative analysis.

Conclusions:

The proposed Bayesian decision theory framework offers a consistent approach to calibration.
This method enhances the understanding of information utilization in calibration experiments.
The approach provides a conceptually simple guideline for analytical chemists dealing with uncertain data.
The study demonstrates the application using a gas chromatography example.