Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.3K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.3K
Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

38.2K
Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
38.2K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

519
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
519
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

684
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
684
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

4.1K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
4.1K
Bootstrapping01:24

Bootstrapping

606
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
606

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Using a 4-Megapixel Hybrid Photon Counting Detector for Fast, Laboratory-Based Nanoscale X-Ray Tomography.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada·2026
Same author

Complex cooperativity in DNA origami revealed via design-dependent defectivity.

Nucleic acids research·2026
Same author

A Phase 1 Dose-Escalation, Food Effect, and Drug-Drug Interaction Study Evaluating the Safety, Tolerability, Pharmacokinetics, and Pharmacodynamics of the MALT1 Inhibitor, SGR-1505, in Healthy Volunteers.

Clinical pharmacology in drug development·2026
Same author

A Metrological Near-Room-Temperature Photon-Number-Resolving Detector: A Design Study.

Sensors (Basel, Switzerland)·2025
Same author

Prediction of Extreme Value Areal Parameters in Laser Powder Bed Fusion of Nickel Superalloy 625.

Surface topography : metrology and properties·2024
Same author

Proposed experiment to measure nonlinear optical susceptibilities in the saturated regime.

Physical review. A·2024

Related Experiment Video

Updated: Jul 2, 2025

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

2.5K

Linearity Characterization and Uncertainty Quantification of Spectroradiometers via Maximum Likelihood and the

Adam L Pintar1, Zachary H Levine2, Howard W Yoon3

  • 1Statistical Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899-8980 USA.

Metrologia
|February 21, 2024
PubMed
Summary

This study introduces a robust uncertainty quantification method for the flux-addition technique, enhancing radiometric instrument linearity calibration. The new approach ensures accurate flux and coefficient estimation with reliable confidence intervals.

Keywords:
BootstrapCalibrationMaximum LikelihoodSatellite Based MeasurementsSpectroradiometer

More Related Videos

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

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

Published on: September 7, 2019

8.3K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

Related Experiment Videos

Last Updated: Jul 2, 2025

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

2.5K
Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

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

Published on: September 7, 2019

8.3K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

Area of Science:

  • Metrology and Scientific Instrumentation
  • Radiometry and Photometry
  • Statistical Modeling and Data Analysis

Background:

  • Radiometric instruments require accurate linearity characterization and correction for reliable measurements.
  • Existing techniques, such as the flux-addition method (combinatorial technique), lack rigorous uncertainty quantification.
  • Nonlinear responses in instruments can significantly impact measurement accuracy.

Purpose of the Study:

  • To develop and validate a rigorous uncertainty quantification method for the flux-addition technique.
  • To apply the method to both synthetic and experimental data from a beam conjoiner instrument.
  • To enable precise calibration of radiometric instruments, including estimation of nonlinear response uncertainties.

Main Methods:

  • Development of a probabilistic model linking instrument readout to unknown fluxes via polynomial coefficients.
  • Utilizing Maximum Likelihood Estimates (MLEs) for unknown fluxes and polynomial coefficients.
  • Employing a non-parametric bootstrap algorithm for uncertainty quantification (standard errors, confidence intervals).

Main Results:

  • Validated the method using synthetic radiometric instrument data, showing approximately unbiased MLEs.
  • Bootstrap-derived confidence intervals demonstrated consistency with the target 95% coverage for fluxes.
  • Observed confidence interval coverages for polynomial coefficients ranged from 91% to 99%.
  • Experimental data demonstrated complete calibration with uncertainties, with nonlinear response uncertainty <0.025%.

Conclusions:

  • The developed uncertainty quantification method provides a rigorous framework for the flux-addition technique.
  • The method effectively characterizes and corrects radiometric instrument linearity with quantifiable uncertainties.
  • This approach significantly enhances the reliability and accuracy of radiometric measurements.