Jove
Visualize
Contact Us

Related Concept Videos

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

737
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
737
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

35
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
35
Organic Compounds03:02

Organic Compounds

51.4K
All living things are formed mostly of carbon compounds called organic compounds. The category of organic compounds includes both natural and synthetic compounds that contain carbon. Although a single, precise definition has yet to be identified by the chemistry community, most agree that a defining trait of organic molecules is the presence of carbon as the principal element, bonded to hydrogen and other carbon atoms. However, some carbon-containing compounds such as carbonates, cyanides, and...
51.4K
Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

738
Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
738
Classification of Elements and Compounds02:54

Classification of Elements and Compounds

66.5K
Pure substances consist of only one type of matter. A pure substance can be an element or a compound. An element consists of only one type of atom, while a compound consists of two or more types of atoms held together by a chemical bond. Elements are classified as atomic or molecular based on the nature of their basic units.
Compounds are pure substances composed of two or more elements in fixed, definite proportions. Compounds are classified as ionic or molecular (covalent) based on the bonds...
66.5K

You might also read

Related Articles

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

Sort by
Same author

Synthesis and machine learning techniques to enable data-driven investigation of supramolecular host-guest interactions.

Chemical science·2026
Same author

Long-Range Transport of Oil by Marine Plastic Debris: Evidence from an 8500 km Journey.

Environmental science & technology·2026
Same author

Exploring the limits of inductive electron withdrawal in fused bicyclic azaheterocycles.

Chemical communications (Cambridge, England)·2025
Same author

Development of electronically tuneable N-heterocyclic borates.

Chemical communications (Cambridge, England)·2025
Same author

Chemical fingerprinting and weathering assessment of Sanchi oil tanker spill residues that impacted the southern Japanese islands.

Marine pollution bulletin·2025
Same author

Carbonates identified by the Curiosity rover indicate a carbon cycle operated on ancient Mars.

Science (New York, N.Y.)·2025
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 Experiment Video

Updated: Jun 18, 2025

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures
09:38

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Published on: January 7, 2019

8.6K

Machine Learning in Complex Organic Mixtures: Applying Domain Knowledge Allows for Meaningful Performance with Small

Katelyn Le1, Jagoš R Radović2, Justin L MacCallum1

  • 1Department of Chemistry, University of Calgary, Calgary, Alberta T2N 1N4, Canada.

Journal of the American Chemical Society
|July 31, 2024
PubMed
Summary

This study demonstrates that incorporating domain knowledge into machine learning models improves their performance on small environmental datasets. This approach enhances the analysis of complex mixtures, even with limited data.

More Related Videos

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

1.2K
High-Throughput Measurement and Classification of Organic P in Environmental Samples
08:58

High-Throughput Measurement and Classification of Organic P in Environmental Samples

Published on: June 8, 2011

13.0K

Related Experiment Videos

Last Updated: Jun 18, 2025

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures
09:38

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Published on: January 7, 2019

8.6K
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

1.2K
High-Throughput Measurement and Classification of Organic P in Environmental Samples
08:58

High-Throughput Measurement and Classification of Organic P in Environmental Samples

Published on: June 8, 2011

13.0K

Area of Science:

  • Chemistry
  • Environmental Science
  • Data Science

Background:

  • Quantifying components in complex mixtures is a persistent challenge across scientific disciplines.
  • Machine learning (ML) has driven advances in "omics" fields but is underutilized in other chemistry areas due to small datasets.
  • Environmental samples often yield limited data, hindering conventional ML applications.

Purpose of the Study:

  • To develop an effective ML approach for analyzing complex mixtures using small environmental datasets.
  • To demonstrate the impact of domain knowledge on ML model performance in data-scarce scenarios.

Main Methods:

  • Utilized a small dataset of 35 high-resolution mass spectra from Canadian petroleum fractions.
  • Applied machine learning models integrated with specific domain knowledge.
  • Compared performance against unconstrained ML approaches.

Main Results:

  • ML models incorporating domain knowledge achieved notable performance.
  • The approach proved effective even with a limited number of spectra.
  • Demonstrated successful analysis of complex mixtures from environmental samples.

Conclusions:

  • Specific domain knowledge significantly enhances ML model performance for complex mixture analysis.
  • This methodology offers a viable solution for analyzing small environmental datasets.
  • The findings pave the way for broader ML adoption in environmental chemistry.