Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Calibration Curves: Linear Least Squares

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...

You might also read

Related Articles

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

Sort by

Same author

A Cross-Sectional Survey of Infection Prevention and Control in Funeral Facilities - 19 PLADs, China, 2024.

China CDC weekly·2026

Same author

Dimensional engineering of 3D hierarchical camellia-like supramolecular organic frameworks for enhanced electrochemiluminescence.

Chemical communications (Cambridge, England)·2026

Same author

DAXX governs the silencing of LINE1 during spermatogenesis in mice.

Nucleic acids research·2026

Same author

High-throughput visual detection of multiple sweat biomarkers based on Au@Pt modified bimetallic MOF-derived nanozyme hydrogels.

Chemical communications (Cambridge, England)·2026

Same author

OCT-Derived Virtual Fractional Flow Reserve Associated With 1-Year Outcomes After PCI in ACS Patients.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions·2026

Same author

Device Simplification: Example of the Application of a Bifunctional Molecularly Imprinted Polymer Membrane in a Visual Photoelectrochromic Chip.

Analytical chemistry·2026

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

Search research articles

Related Experiment Video

Updated: May 17, 2026

Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy

Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy

Published on: January 9, 2017

Analysis of noisy dynamic light scattering data using constrained regularization techniques.

Xinjun Zhu¹, Jin Shen, John C Thomas

¹School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, China.

|November 7, 2012

Summary

This summary is machine-generated.

Noise in dynamic light scattering (DLS) data hinders accurate particle size distribution (PSD) analysis. This study introduces novel baseline error compensation strategies for improved PSD recovery from noisy DLS measurements.

More Related Videos

Controlled Synthesis and Fluorescence Tracking of Highly Uniform Poly(N-isopropylacrylamide) Microgels

Controlled Synthesis and Fluorescence Tracking of Highly Uniform Poly(N-isopropylacrylamide) Microgels

Published on: September 8, 2016

Application of Voltage in Dynamic Light Scattering Particle Size Analysis

Application of Voltage in Dynamic Light Scattering Particle Size Analysis

Published on: January 24, 2020

Related Experiment Videos

Last Updated: May 17, 2026

Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy

Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy

Published on: January 9, 2017

Controlled Synthesis and Fluorescence Tracking of Highly Uniform Poly(N-isopropylacrylamide) Microgels

Controlled Synthesis and Fluorescence Tracking of Highly Uniform Poly(N-isopropylacrylamide) Microgels

Published on: September 8, 2016

Application of Voltage in Dynamic Light Scattering Particle Size Analysis

Application of Voltage in Dynamic Light Scattering Particle Size Analysis

Published on: January 24, 2020

Area of Science:

Colloid and interface science
Materials characterization
Data analysis and modeling

Background:

Dynamic light scattering (DLS) is crucial for determining particle size distribution (PSD).
Noisy DLS data, particularly with baseline errors, compromises the reliability of PSD inversion.
Accurate PSD recovery is essential for understanding colloidal system behavior.

Purpose of the Study:

To develop and evaluate methods for compensating baseline errors in DLS correlation functions.
To improve the accuracy of particle size distribution (PSD) recovery from poor-quality DLS data.
To compare different regularization parameter selection rules for noisy DLS analysis.

Main Methods:

Constrained regularization techniques were applied to analyze DLS data with baseline errors.
Two novel strategies were proposed: edge proportion detection and solution norm-based compensation.
Various regularization parameter selection rules (L-curve, GCV, robust GCV) were investigated.

Main Results:

The proposed strategies effectively compensated for baseline errors in simulated and experimental DLS data.
Baseline correction and appropriate regularization parameter choice significantly enhanced PSD recovery accuracy.
The study demonstrated the effectiveness of the developed methods for handling noisy DLS measurements.

Conclusions:

Constrained regularization with baseline compensation offers a robust solution for analyzing noisy DLS data.
Accurate PSD determination from poor-quality DLS data is achievable with the proposed methods.
This work provides valuable tools for reliable characterization of colloidal particles using DLS.