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...

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

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...

Residuals and Least-Squares Property

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...

Weighted Mean

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...

You might also read

Related Articles

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

Sort by

Same author

Application of fast Fourier transform cross-correlation and mass spectrometry data for accurate alignment of chromatograms.

Journal of chromatography. A·2013

Same author

Nonlinear alignment of chromatograms by means of moving window fast Fourier transfrom cross-correlation.

Journal of separation science·2013

Same author

6-oxy-(acetyl piperazine) fluorescein as a new fluorescent labeling reagent for free fatty acids in serum using high-performance liquid chromatography.

Journal of chromatography. A·2007

Same author

Synthesis and fluorescence properties of 5,7-diphenylquinoline and 2,5,7-triphenylquinoline derived from m-terphenylamine.

Molecules (Basel, Switzerland)·2007

Same author

[Metabolic engineering of terpenoids in plants].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology·2007

Same author

Hedgehog signaling in the murine melanoma microenvironment.

Angiogenesis·2007

Search research articles

Related Experiment Video

Updated: May 10, 2026

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

Morphological weighted penalized least squares for background correction.

Zhong Li¹, De-Jian Zhan, Jia-Jun Wang

¹Yunnan Academy of Tobacco Science, Kunming 650106, PR China.

|June 20, 2013

Summary

A new automatic background correction method using morphological operations and weighted penalized least squares (MPLS) effectively removes background noise from analytical signals without prior knowledge or manual intervention.

More Related Videos

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Related Experiment Videos

Last Updated: May 10, 2026

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

Analytical Chemistry
Signal Processing
Computational Methods

Background:

Analytical signals often contain background noise, which reduces the effectiveness, selectivity, and sensitivity of analytical methods.
Accurate background correction is crucial for reliable qualitative and quantitative analysis.
Existing methods may require prior knowledge, manual intervention, or iterative procedures.

Purpose of the Study:

To develop a novel, automatic method for background correction in analytical signals.
To address the limitations of existing background correction techniques.
To provide a robust and flexible solution for diverse analytical data.

Main Methods:

Development of a new background correction algorithm based on morphological operations.
Integration of weighted penalized least squares (MPLS) for signal processing.
Implementation as an open-source package for accessibility.

Main Results:

The proposed method successfully corrected backgrounds in both simulated and experimental datasets.
The method demonstrated flexibility in handling various types of background interferences.
No prior knowledge of the background, iteration, or manual selection was required.

Conclusions:

The developed automatic background correction method (MPLS) is effective and versatile.
This approach enhances the reliability of analytical methods by improving signal quality.
The open-source availability promotes wider adoption and further research.