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

Racemic Mixtures and the Resolution of Enantiomers02:30

Racemic Mixtures and the Resolution of Enantiomers

20.3K
A racemic mixture, or racemate, is an equimolar mixture of enantiomers of a molecule that can be separated using their unique interaction with chiral molecules or media. Racemic mixtures are denoted by the (±)- prefix. This ‘optical rotation descriptor’ applies to the whole solution of a racemic mixture rather than a specific stereoisomer. Enantiomers typically have the same physical and chemical properties. Hence, they are not easily separable. However, enantiomers can exhibit...
20.3K
Multiple Regression01:25

Multiple Regression

3.5K
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...
3.5K
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

328
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
328
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

259
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 of...
259
Curve Equations01:17

Curve Equations

189
Curves are essential geometric elements characterized by tangent distance, chord length, middle ordinate, and total arc length. These measurements are crucial in understanding a curve's geometric and spatial properties and are defined by the relationship between its radius and its central angle.The tangent distance (T) refers to the straight-line measurement from the intersection point of two tangents to either the start or end of the curve. This distance is influenced by the curve's radius (R)...
189
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

3.7K
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...
3.7K

You might also read

Related Articles

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

Sort by
Same author

Regions of interest multivariate curve resolution for liquid chromatography with ion mobility high-resolution tandem mass spectrometry: analysis of plastic additives.

Analytical and bioanalytical chemistry·2026
Same author

Fusion of incomplete QCL-IR and ECD datasets using MCR-ALS to extend the viable concentration range for studying protein denaturation.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2025
Same author

Hyperspectral image and chemometrics. A step beyond classical spectroscopic PAT tools.

Analytical and bioanalytical chemistry·2025
Same author

Non-target metabolomic approach of the toxic effects of glyphosate in zebrafish (D. rerio).

Environmental research·2025
Same author

Coupling electrochemical and spectroscopic methods for river water dissolved organic matter characterization.

Environmental monitoring and assessment·2025
Same author

Analysis of ischemic intestinal tissue composition based on visible and near-infrared reflectance hyperspectral imaging and multivariate curve resolution.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2025

Related Experiment Video

Updated: Nov 21, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.1K

Multivariate Curve Resolution: 50 years addressing the mixture analysis problem - A review.

Anna de Juan1, Romà Tauler2

  • 1Chemometrics Group. Universitat de Barcelona. Dept. of Chemical Engineering and Analytical Chemistry, Martí I Franquès, 1, 08028, Barcelona, Spain.

Analytica Chimica Acta
|January 17, 2021
PubMed
Summary

Multivariate Curve Resolution (MCR) provides algorithms for mixture analysis using bilinear models. This mature methodology continuously evolves, adapting to new scientific challenges and data structures for enhanced data interpretation.

Keywords:
-Omics data analysisAmbiguityBig dataConstraintsEnvironmental data analysisHyperspectral image analysisMultidimensional chromatographyMultiset analysisMultivariate curve resolutionProcess analysis

More Related Videos

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.2K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.8K

Related Experiment Videos

Last Updated: Nov 21, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.1K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.2K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.8K

Area of Science:

  • Chemometrics
  • Data Analysis
  • Analytical Chemistry

Background:

  • Multivariate Curve Resolution (MCR) addresses the challenge of analyzing complex mixtures.
  • The core principle involves decomposing data into pure component contributions using bilinear models.

Purpose of the Study:

  • To provide an overview of the evolution and current state of Multivariate Curve Resolution (MCR).
  • To highlight the adaptability and ongoing development of MCR methods in diverse scientific fields.

Main Methods:

  • Review of foundational Multivariate Curve Resolution (MCR) algorithms and their theoretical underpinnings.
  • Discussion of advancements including hybrid bilinear/multilinear models and handling of irregular data structures.

Main Results:

  • MCR has evolved significantly since 1971, incorporating new constraints and modeling tasks.
  • The methodology now accommodates complex data types, including incomplete multisets and combined matrix/tensor data.

Conclusions:

  • Multivariate Curve Resolution (MCR) is a robust and adaptable methodology for mixture analysis.
  • Despite its maturity, MCR continues to advance, offering potential for future developments in scientific data interpretation.