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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.7K

You might also read

Related Articles

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

Sort by
Same author

Shifts in the Lung Microbiota and Antibiotic Resistance Genes Occur With Aging in Patients With Lower Respiratory Tract Infections.

BioMed research international·2026
Same author

Noncanonical role of KDM5C in conferring bortezomib resistance via the PERK‒Nrf2 axis in multiple myeloma.

Cell death & disease·2026
Same author

Fifty Years and Counting: Searching for the "Silver Bullet" or the "Silver Shotgun" to Mitigate Preharvest Aflatoxin Contamination.

Toxins·2025
Same author

Population-specific pangenome unveils a third FAD2 gene and solves the peanut mid-oleic fatty acid mystery.

Nature communications·2025
Same author

Discovery of a Novel DNMT1 Inhibitor with Improved Efficacy in Treating β-Thalassemia.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

Accelerated Super-Resolution Reconstruction for Structured Illumination Microscopy Integrated with Low-Light Optimization.

Micromachines·2025
Same journal

Multifunctional reconfigurable terahertz metasurface based on vanadium dioxide phase transition: achieving broadband absorption and efficient polarization conversion.

Applied optics·2026
Same journal

High-Q-factor electromagnetically induced transparency utilizing quasi-bound states in the continuum in an all-dielectric terahertz metasurface.

Applied optics·2026
Same journal

Automated stitching interferometry for high-precision metrology of X-ray mirrors.

Applied optics·2026
Same journal

Experimental demonstration of an approach to designing a metal-dielectric DBR resonant cavity structure.

Applied optics·2026
Same journal

High-precision wavefront reconstruction from a single-shot interferogram using a physics-driven hybrid feature calibration network.

Applied optics·2026
Same journal

Ultra-high-Q Fano resonance based on coupled topological corner states in Kagome photonic crystals.

Applied optics·2026
See all related articles

Related Experiment Video

Updated: Jul 31, 2025

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research
05:22

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

464

Differential confocal over-range determination method based on an information theory.

Tao Yuan, Dingrong Yi, Yiqing Ye

    Applied Optics
    |May 3, 2023
    PubMed
    Summary
    This summary is machine-generated.

    A new differential confocal over-range determination method (IT-ORDM) accurately identifies if sample surface heights are within the effective measurement range. This ensures reliable 3D shape restoration for differential confocal axial measurements.

    More Related Videos

    Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy
    06:51

    Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

    Published on: August 2, 2018

    7.2K
    Author Spotlight: Mitochondrial Remodeling in Skeletal Muscle
    10:53

    Author Spotlight: Mitochondrial Remodeling in Skeletal Muscle

    Published on: December 1, 2023

    3.5K

    Related Experiment Videos

    Last Updated: Jul 31, 2025

    Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research
    05:22

    Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

    Published on: June 21, 2024

    464
    Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy
    06:51

    Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

    Published on: August 2, 2018

    7.2K
    Author Spotlight: Mitochondrial Remodeling in Skeletal Muscle
    10:53

    Author Spotlight: Mitochondrial Remodeling in Skeletal Muscle

    Published on: December 1, 2023

    3.5K

    Area of Science:

    • Optical Metrology
    • 3D Surface Profilometry
    • Confocal Microscopy

    Background:

    • Existing differential confocal axial 3D measurement lacks a method to verify if sample surface heights are within the effective measurement range.
    • This limitation hinders accurate 3D shape determination and data reliability.

    Purpose of the Study:

    • To propose and validate an information theory-based differential confocal over-range determination method (IT-ORDM).
    • To enable precise determination of whether sample surface height data falls within the effective measurement range of differential confocal axial measurements.

    Main Methods:

    • The IT-ORDM establishes the axial effective measurement range boundaries using the differential confocal axial light intensity response curve.
    • It determines effective intensity measurement ranges for pre-focus and post-focus axial response curves (ARC).
    • An intersection operation on effective measurement pre-focus and post-focus images extracts the differential confocal image's effective measurement area.

    Main Results:

    • The IT-ORDM successfully identifies the boundaries of the axial effective measurement range.
    • It accurately determines the effective measurement ranges for pre-focus and post-focus ARCs.
    • Experimental results demonstrate effective determination and restoration of the 3D sample surface shape at the reference plane.

    Conclusions:

    • The proposed IT-ORDM effectively addresses the limitation of existing differential confocal axial 3D measurement methods.
    • This method ensures the reliability of 3D surface height measurements by confirming data is within the effective range.
    • IT-ORDM facilitates accurate 3D shape restoration of sample surfaces.