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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.
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Instrument Calibration

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Related Experiment Video

Updated: Jun 1, 2026

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

Elimination of uninformative variables for multivariate calibration.

V Centner¹, D L Massart, O E de Noord

¹ChemoAC, Vrije Universiteit Brussel, Laarbeeklaan 103, B-1090 Brussel, Belgium.

Analytical Chemistry

|May 31, 2011

Summary

This study introduces a novel method to remove unimportant variables from complex datasets by adding artificial noise variables. This technique enhances predictive accuracy by identifying and eliminating less significant experimental variables.

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Basics of Multivariate Analysis in Neuroimaging Data

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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

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O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

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Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

Chemometrics
Data Science
Multivariate Analysis

Background:

Multivariate data analysis often includes uninformative variables that can hinder model performance.
Variable selection is crucial for improving the efficiency and accuracy of predictive models.
Existing methods may not effectively handle the complexity of large datasets.

Purpose of the Study:

To propose a new method for the effective elimination of uninformative variables in multivariate datasets.
To enhance the predictive ability of models by reducing data dimensionality.
To provide a robust approach applicable to both simulated and real-world data.

Main Methods:

Introduction of artificial (noise) variables into the dataset.
Obtaining a closed-form Partial Least Squares (PLS) or Principal Component Regression (PCR) model.
Utilizing a criterion based on 'b' coefficients to assess variable importance relative to artificial variables.

Main Results:

Experimental variables deemed less important than artificial variables are successfully eliminated.
The method's performance is validated using simulated datasets.
Practical application is demonstrated on near-infrared (near-IR) spectroscopic data.

Conclusions:

The proposed method effectively identifies and removes uninformative variables.
Eliminating uninformative variables leads to improved predictive performance.
The technique offers a valuable tool for data preprocessing in chemometrics and related fields.