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

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...
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...

You might also read

Related Articles

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

Sort by
Same author

Phase-shifting interferometry corrupted by white and non-white additive noise.

Optics express·2011
Same author

Design of phase-shifting algorithms by fine-tuning spectral shaping.

Optics express·2011
Same author

Fast optical source for quantum key distribution based on semiconductor optical amplifiers.

Optics express·2011
Same author

Stable-marriages algorithm for preprocessing phase maps with discontinuity sources.

Applied optics·2010
Same author

Phase-unwrapping algorithm based on an adaptive criterion.

Applied optics·2010
Same author

Improved linear programming method to generate metameric spectral distributions.

Applied optics·2010
Same journal

Multifunctional reconfigurable terahertz metasurface based on vanadium dioxide phase transition: achieving broadband absorption and efficient polarization conversion.

Applied optics·2026
Same journal

High-Q-factor electromagnetically induced transparency utilizing quasi-bound states in the continuum in an all-dielectric terahertz metasurface.

Applied optics·2026
Same journal

Automated stitching interferometry for high-precision metrology of X-ray mirrors.

Applied optics·2026
Same journal

Experimental demonstration of an approach to designing a metal-dielectric DBR resonant cavity structure.

Applied optics·2026
Same journal

High-precision wavefront reconstruction from a single-shot interferogram using a physics-driven hybrid feature calibration network.

Applied optics·2026
Same journal

Ultra-high-Q Fano resonance based on coupled topological corner states in Kagome photonic crystals.

Applied optics·2026
See all related articles

Related Experiment Video

Updated: Jun 8, 2026

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

Colorimetric matching by minimum-square-error fitting.

J A Quiroga, J Zoido, J Alonso

    Applied Optics
    |October 12, 2010
    PubMed
    Summary
    This summary is machine-generated.

    Least-squares approximation improves image-based colorimetry accuracy. A new calculation method offers better results than previous research for accurate color measurement.

    More Related Videos

    Qualitative Identification of Carboxylic Acids, Boronic Acids, and Amines Using Cruciform Fluorophores
    09:46

    Qualitative Identification of Carboxylic Acids, Boronic Acids, and Amines Using Cruciform Fluorophores

    Published on: August 19, 2013

    Related Experiment Videos

    Last Updated: Jun 8, 2026

    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

    Qualitative Identification of Carboxylic Acids, Boronic Acids, and Amines Using Cruciform Fluorophores
    09:46

    Qualitative Identification of Carboxylic Acids, Boronic Acids, and Amines Using Cruciform Fluorophores

    Published on: August 19, 2013

    Area of Science:

    • Colorimetry
    • Image Processing
    • Computational Science

    Background:

    • Image-based colorimetry relies on accurate data sampling.
    • Least-squares approximation is a common technique for data analysis.

    Purpose of the Study:

    • To evaluate the accuracy of least-squares approximation in image-based colorimetry.
    • To introduce and compare a novel calculation method for improved accuracy.

    Main Methods:

    • Selection of appropriate sampling functions for colorimetric data.
    • Application of least-squares approximation for data fitting.
    • Comparison of a new calculation method against established techniques.

    Main Results:

    • The accuracy of earlier research using least-squares approximation was analyzed.
    • The novel calculation method demonstrated superior accuracy in colorimetric measurements.
    • The choice of sampling functions significantly impacts approximation accuracy.

    Conclusions:

    • Least-squares approximation is a viable tool for image-based colorimetry.
    • The proposed calculation method offers enhanced precision for color measurement applications.
    • Further research into sampling function optimization is recommended for colorimetric accuracy.