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

Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

2.3K
Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
2.3K
Mass Analyzers: Overview01:13

Mass Analyzers: Overview

1.6K
The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
1.6K
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

8.1K
Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
8.1K
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

1.5K
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...
1.5K
High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

2.3K
The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For...
2.3K

You might also read

Related Articles

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

Sort by
Same author

Molecular-level traits of root exudates in tropical forest trees reflect nitrogen-fixation strategy and phenological shifts.

The New phytologist·2026
Same author

Targeting amino acid metabolism in hepatocellular carcinoma.

Trends in molecular medicine·2026
Same author

Etiology-Based Treatment for Unresectable/Advanced Hepatocellular Carcinoma: Focus on Viral Hepatitis and Metabolic Dysfunction-Associated Steatohepatitis.

Liver cancer·2026
Same author

Electronics-free soft robotic minitablet for on-demand gastric molecular sensing and diagnostics in vivo.

Science advances·2026
Same author

BCAT1-dependent HIF-1α stabilization is a targetable metabolic vulnerability in hepatocellular carcinoma.

Cell reports. Medicine·2026
Same author

Applying asymmetric-waveform alternating current in nickel-catalyzed asymmetric reductive cross-coupling.

Nature communications·2026

Related Experiment Video

Updated: Jan 10, 2026

Shotgun Lipidomics of Rodent Tissues
11:46

Shotgun Lipidomics of Rodent Tissues

Published on: November 18, 2022

2.6K

Resolution-Adaptive Binning Enhances Machine Learning Modeling by Interbatch and Multiplatform Orbitrap-Based Shotgun

Hiu-Lok Ngan1, Jialing Zhang1, Kenneth Kin-Leung Kwan2,3

  • 1State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong 999077, P. R. China.

Analytical Chemistry
|November 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel mass resolution-adaptive binning strategy to integrate mass spectrometry data across different batches and platforms. The method improves machine learning model generalizability for disease detection, enhancing accuracy in various sample types.

More Related Videos

Quantitative Proteomics Workflow using Multiple Reaction Monitoring Based Detection of Proteins from Human Brain Tissue
11:49

Quantitative Proteomics Workflow using Multiple Reaction Monitoring Based Detection of Proteins from Human Brain Tissue

Published on: August 28, 2021

5.0K
Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
07:01

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

785

Related Experiment Videos

Last Updated: Jan 10, 2026

Shotgun Lipidomics of Rodent Tissues
11:46

Shotgun Lipidomics of Rodent Tissues

Published on: November 18, 2022

2.6K
Quantitative Proteomics Workflow using Multiple Reaction Monitoring Based Detection of Proteins from Human Brain Tissue
11:49

Quantitative Proteomics Workflow using Multiple Reaction Monitoring Based Detection of Proteins from Human Brain Tissue

Published on: August 28, 2021

5.0K
Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
07:01

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

785

Area of Science:

  • Analytical Chemistry
  • Computational Biology
  • Biomedical Science

Background:

  • Machine learning (ML) on mass spectrometry (MS) data aids disease modeling but faces challenges with batch-specific models and limited generalizability.
  • Traditional data integration methods struggle with mass-to-charge (m/z) features, hindering reliable data combination across different MS batches and platforms.
  • The need for robust data integration strategies is critical for advancing ML applications in disease diagnostics using MS.

Purpose of the Study:

  • To develop and validate a mass resolution-adaptive binning and integration strategy for MS data.
  • To enhance the transferability and generalizability of ML models across diverse MS datasets and platforms.
  • To improve disease detection accuracy by enabling reliable integration of multi-batch and multi-platform MS data.

Main Methods:

  • A novel mass resolution-adaptive binning strategy was developed to handle m/z features across varying mass spectrometry resolutions.
  • The strategy was tested on mixed standard solutions, recovering 88-99% of ground truth features in the low mass region.
  • The approach was applied to a mouse model of hepatocellular carcinoma, integrating ambient MS imaging (MSI) and shotgun proteomics data.

Main Results:

  • The proposed method demonstrated stable binning and integration across low, mid, and high mass regions, outperforming conventional techniques.
  • Predictive models built using the integrated data showed superior performance compared to those using conventional methods.
  • In a hepatocellular carcinoma mouse model, 10 generic metabolites were identified, leading to accurate disease detection via MSI (F1 score = 0.87) and direct infusion (recall = 0.89).

Conclusions:

  • The mass resolution-adaptive binning and integration strategy effectively overcomes limitations of traditional methods for MS data integration.
  • This novel approach significantly improves the generalizability of ML models for disease detection across various sample introduction methods.
  • The strategy holds promise for advancing MS-based diagnostics by enabling more accurate and reliable integration of diverse datasets.