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

Introduction to z Scores01:06

Introduction to z Scores

11.2K
A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
11.2K
Introduction to z Scores01:05

Introduction to z Scores

1.3K
A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
1.3K
z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

19.6K
z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
19.6K
z Scores and Unusual Values01:07

z Scores and Unusual Values

11.0K
The z score is one of the three measures of relative standing. It describes the location of a value in a dataset relative to the mean. z scores are obtained after the standardization of the values in a dataset. The z score for the mean is 0.
 This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data...
11.0K
Light Acquisition02:16

Light Acquisition

9.6K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
9.6K
Water and Mineral Acquisition02:34

Water and Mineral Acquisition

35.7K
Specialized tissues in plant roots have evolved to capture water, minerals, and some ions from the soil. Roots exhibit a variety of branching patterns that facilitate this process. The outermost root cells have specialized structures called root hairs that increase the root surface, thus increasing soil contact. Water can passively cross into roots, as the concentration of water in the soil is higher than that of the root tissue. Minerals, in contrast, are actively transported into root cells.
35.7K

You might also read

Related Articles

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

Sort by
Same author

Normalized peak distribution uniformity metrics for chromatographic evaluation.

Journal of chromatography. A·2026
Same author

A Protocol for Characterizing Comprehensive Two-Dimensional Liquid Chromatography Systems.

Journal of separation science·2026
Same author

Retention modeling of oligonucleotides on an amide-based HILIC column: A descriptor-driven approach.

Journal of chromatography. A·2026
Same author

Comprehensive evaluation of the analytical toolbox for commercial cysteine-linked antibody-drug conjugates.

Journal of chromatography. A·2026
Same author

Simultaneous analysis of various anticancer drugs by supercritical fluid chromatography-mass spectrometry. Part II: Method validation and comparison with liquid chromatography.

Journal of pharmaceutical and biomedical analysis·2026
Same author

Benchmarking size-exclusion chromatography columns for the analysis of therapeutic peptides and model oligonucleotides.

Journal of chromatography. A·2026

Related Experiment Video

Updated: Jan 30, 2026

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

935

A scoring approach for multi-platform acquisition in metabolomics.

Julian Pezzatti1, Víctor González-Ruiz2, Santiago Codesido1

  • 1School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, CMU - Rue Michel Servet 1, 1211 Geneva, Switzerland.

Journal of Chromatography. A
|January 28, 2019
PubMed
Summary

Researchers developed a scoring method to evaluate liquid chromatography-mass spectrometry (LC-MS) conditions for metabolomics. This approach optimizes analytical strategies, achieving high metabolite coverage with fewer LC-MS methods.

Keywords:
MetabolomicsMethod selectionMulti-modal analysisUHPLC-HRMS

More Related Videos

A Multi-compartment CNS Neuron-glia Co-culture Microfluidic Platform
13:24

A Multi-compartment CNS Neuron-glia Co-culture Microfluidic Platform

Published on: September 10, 2009

12.4K
The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
13:02

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

11.0K

Related Experiment Videos

Last Updated: Jan 30, 2026

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

935
A Multi-compartment CNS Neuron-glia Co-culture Microfluidic Platform
13:24

A Multi-compartment CNS Neuron-glia Co-culture Microfluidic Platform

Published on: September 10, 2009

12.4K
The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
13:02

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

11.0K

Area of Science:

  • Analytical Chemistry
  • Metabolomics
  • Biochemistry

Background:

  • Untargeted metabolomics aims for broad metabolite analysis, necessitating robust evaluation of multi-platform strategies.
  • Selecting optimal analytical conditions is crucial for maximizing metabolite identification and data quality.

Purpose of the Study:

  • To develop a scoring approach for comparing and selecting liquid chromatography-mass spectrometry (LC-MS) conditions in metabolomics.
  • To assess the performance of different LC-MS platforms using a comprehensive metabolite library.

Main Methods:

  • A novel scoring approach was developed, integrating chromatographic and mass spectrometry (MS) peak attributes (retention, signal-to-noise ratio, peak intensity, shape).
  • Two reversed-phase liquid chromatography (RPLC) and three hydrophilic interaction liquid chromatography (HILIC) methods coupled with high-resolution mass spectrometry (HRMS) were evaluated.
  • A chemical library of 597 metabolites was used as a benchmark for performance evaluation across positive and negative ionization modes.

Main Results:

  • The scoring approach effectively evaluated individual analytical platforms and optimized the number of methods required.
  • A combination of three optimized LC-MS methods and ionization modes achieved nearly 95% coverage of detected compounds.
  • The performance of three selected methods was comparable to that of five LC-MS conditions.

Conclusions:

  • The developed scoring method provides a quantitative basis for selecting optimal LC-MS conditions in metabolomics.
  • Efficient analytical strategies can be designed by minimizing the number of LC-MS methods without compromising metabolite coverage.
  • This approach enhances the efficiency and effectiveness of untargeted metabolomics studies.