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

Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Regression Analysis01:11

Regression Analysis

5.6K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.6K
Multiple Regression01:25

Multiple Regression

2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.2K
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...
1.2K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

1.5K
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...
1.5K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

94
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
94

You might also read

Related Articles

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

Sort by
Same author

Perspective on the Capacity of the Rashomon Effect in Multivariate Data Analysis.

Applied spectroscopy·2025
Same author

Redefining Spectral Data Analysis with Immersive Analytics: Exploring Domain-Shifted Model Spaces for Optimal Model Selection.

Applied spectroscopy·2024
Same author

Local Modeling by Adapting Source Calibration Models to Analyte Shifted Target Domain Samples Without Reference Values.

Applied spectroscopy·2024
Same author

Physicochemical Responsive Integrated Similarity Measure (PRISM) for a Comprehensive Quantitative Perspective of Sample Similarity Dynamically Assessed with NIR Spectra.

Analytical chemistry·2023
Same author

Calibration Model Updating to Novel Sample and Measurement Conditions without Reference Values.

Analytical chemistry·2021
Same author

Reliable Model Selection without Reference Values by Utilizing Model Diversity with Prediction Similarity.

Journal of chemical information and modeling·2021
Same journal

EXPRESS: Deterministic Compressed Sensing in Time-Domain Spectroscopy.

Applied spectroscopy·2026
Same journal

EXPRESS: Multi-Parameter Wavelength Characterization of Array Spectrometers Under Near-Limit Sampling Conditions.

Applied spectroscopy·2026
Same journal

EXPRESS: A Validated Reference Database for Twentieth-Century Cd-Based Pigments: Integrated Structural and Compositional Characterization.

Applied spectroscopy·2026
Same journal

EXPRESS: Two-Trace Two-Dimensional (2T2D-COS) in the Analysis of Brain Tissue Sample Preparation Method.

Applied spectroscopy·2026
Same journal

EXPRESS: Simplified Protocol for Analyzing Polarization Properties of Scanning Tunneling Microscope (STM) Light Emission Spectra at an Oblique Angle.

Applied spectroscopy·2026
Same journal

EXPRESS: Monitoring a Polyurethane Synthesis by Fiber-Coupled Attenuated Total Reflection Fourier Transform Infrared Spectroscopy and Multivariate Curve Resolution-Alternating Least Squares.

Applied spectroscopy·2026
See all related articles

Related Experiment Video

Updated: Apr 8, 2026

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM
11:57

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

Published on: December 1, 2016

11.3K

Local Adaptive Fusion Regression (LAFR) for Local Linear Multivariate Calibration: Application to Large Datasets.

Robert Spiers1, John H Kalivas1

  • 1Department of Chemistry, Idaho State University, Pocatello, Idaho, USA.

Applied Spectroscopy
|January 23, 2025
PubMed
Summary
This summary is machine-generated.

Local adaptive fusion regression (LAFR) overcomes matrix effects in calibration models by creating precise local calibration sets. This method improves analyte prediction accuracy across diverse datasets without requiring user expertise.

Keywords:
Local modelingmatrix effectsample selectionsample similarity

More Related Videos

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

3.1K

Related Experiment Videos

Last Updated: Apr 8, 2026

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM
11:57

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

Published on: December 1, 2016

11.3K
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

3.1K

Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Spectroscopy

Background:

  • Linear calibration models struggle with predicting analyte amounts due to matrix effects, which are shifts in measurement profiles caused by uncontrollable factors.
  • Current local modeling approaches fail because similar measurements do not guarantee matched underlying matrix effects or analyte amounts.
  • Accurate analyte quantification is crucial across various scientific and industrial applications.

Purpose of the Study:

  • To introduce a novel procedure, local adaptive fusion regression (LAFR), to address the matrix effect matching problem in calibration.
  • To develop a method that creates highly dense, localized linear calibration sets matched spectrally and by analyte amounts to target samples.
  • To demonstrate the self-optimizing nature of LAFR, eliminating the need for specialized expertise.

Main Methods:

  • LAFR employs paradigm shifts in local modeling to solve the matrix effect matching problem.
  • The procedure self-optimizes input hyperparameters, making it user-friendly.
  • LAFR forms localized linear calibration sets by matching target samples spectrally and by analyte amounts.

Main Results:

  • LAFR's capability to form highly dense, localized calibration sets was verified on diverse Near-Infrared (NIR) datasets.
  • Performance was validated using a nonlinear NIR meat dataset, a multi-step NIR sugarcane dataset, and a large NIR soil database (98,910 samples).
  • The method successfully matched target samples spectrally and by analyte amounts, improving calibration accuracy.

Conclusions:

  • LAFR effectively solves the matrix effect matching problem by creating accurate, localized calibration sets.
  • The method demonstrates broad applicability beyond NIR spectroscopy to other measurement systems affected by matrix effects.
  • LAFR offers a robust and accessible solution for improving analyte quantification in complex samples.