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

Manipulation and Analysis01:21

Manipulation and Analysis

GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
Introduction to GIS01:28

Introduction to GIS

Geographic Information Systems (GIS) are tools for storing, analyzing, and displaying spatial data alongside related attributes. Unlike traditional information systems that address general queries, GIS incorporates spatial components, enabling users to answer "where" and "how far." For example, GIS can process housing data linked to geographic locations like zip codes, allowing insights into population density or housing distribution through thematic maps.GIS integrates technologies such as...
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
pV-Diagrams01:18

pV-Diagrams

The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...

You might also read

Related Articles

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

Sort by
Same author

Tapping the Full Potential of Infrared Spectroscopy for the Analysis of Technical Lignins.

ChemSusChem·2024
Same author

Chemometrics in analytical chemistry-part II: modeling, validation, and applications.

Analytical and bioanalytical chemistry·2018
Same author

Chemometrics in analytical chemistry-part I: history, experimental design and data analysis tools.

Analytical and bioanalytical chemistry·2017
Same author

European Analytical Column No. 42.

Analytical and bioanalytical chemistry·2014
Same author

Prediction of liquid chromatographic retention behavior based on quantum chemical parameters using supervised self organizing maps.

Talanta·2013
Same author

PIXE analysis of PM2.5 and PM(2.5-10) for air quality assessment of Islamabad, Pakistan: application of chemometrics for source identification.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering·2012
Same journal

Microwave synthesis, crystal structure, antioxidant, and antimicrobial study of new 6-heptyl-5,6-dihydrobenzo[4,5]imidazo[1,2-c]quinazoline compound.

Chemistry Central journal·2018
Same journal

Determination of antioxidant and antimicrobial activities of the extracts of aerial parts of Portulaca quadrifida.

Chemistry Central journal·2018
Same journal

Design, synthesis and therapeutic potential of 3-(2-(1H-benzo[d]imidazol-2-ylthio)acetamido)-N-(substituted phenyl)benzamide analogues.

Chemistry Central journal·2018
Same journal

Analysis of coumarin and angelica lactones in smokeless tobacco products.

Chemistry Central journal·2018
Same journal

Novel glitazones as PPARγ agonists: molecular design, synthesis, glucose uptake activity and 3D QSAR studies.

Chemistry Central journal·2018
Same journal

Development of a practical synthesis of etravirine via a microwave-promoted amination.

Chemistry Central journal·2018
See all related articles

Related Experiment Video

Updated: May 22, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Self organising maps for visualising and modelling.

Richard G Brereton1

  • 1School of Chemistry, University of Bristol, Cantocks Close, Bristol BS8 1TS, UK. r.g.brereton@bris.ac.uk.

Chemistry Central Journal
|May 19, 2012
PubMed
Summary
This summary is machine-generated.

Self-Organising Maps (SOMs) offer accessible, powerful data visualization for complex analytical chemistry datasets. They outperform traditional methods like PCA, excelling with non-linear data and reducing outlier influence.

More Related Videos

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
09:49

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Published on: November 18, 2015

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Related Experiment Videos

Last Updated: May 22, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
09:49

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Published on: November 18, 2015

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Area of Science:

  • Analytical Chemistry
  • Data Science
  • Chemometrics

Background:

  • Modern analytical chemistry generates complex datasets from diverse fields like metabolomics and environmental science.
  • Traditional methods like Principal Component Analysis (PCA) and Partial Least Squares (PLS) have limitations with non-linear data and outlier sensitivity.
  • Advancements in computing power make sophisticated methods like Self-Organising Maps (SOMs) more accessible and practical.

Purpose of the Study:

  • To introduce and motivate the use of Self-Organising Maps (SOMs) in analytical chemistry.
  • To highlight the advantages of SOMs over traditional methods for complex, non-linear datasets.
  • To demonstrate the application and visualization capabilities of SOMs through case studies.

Main Methods:

  • Self-Organising Maps (SOMs) for dimensionality reduction and visualization.
  • Comparison of SOMs with Principal Component Analysis (PCA) and Partial Least Squares (PLS).
  • Visualization techniques including best matching units, hit histograms, unified distance matrices, and component planes.
  • Supervised SOMs for classification, multifactor data analysis, and variable selection.

Main Results:

  • SOMs provide intuitive visualization methods, effectively utilizing map space.
  • SOMs are less dependent on least squares solutions, error normality, and are less influenced by outliers compared to PCA and PLS.
  • Supervised SOMs demonstrate utility in classification and Quality Control applications.
  • Case studies confirm SOMs' applicability to diverse analytical challenges.

Conclusions:

  • Self-Organising Maps (SOMs) are a powerful and accessible tool for analyzing complex datasets in modern analytical chemistry.
  • SOMs offer significant advantages over traditional methods, particularly for non-linear data and when dealing with outliers.
  • The visualization and classification capabilities of SOMs, as shown in case studies, make them valuable for various applications including cultural heritage, environmental, metabolomic, and pharmaceutical analysis.