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Related Concept Videos

Calibration Curves: Linear Least Squares01:20

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...
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...
Instrument Calibration01:12

Instrument Calibration

Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Glassware Calibration01:11

Glassware Calibration

Accurate calibration of glassware, such as volumetric flasks, pipettes, and burettes, is essential to ensure accurate measurements in the analytical laboratory. Calibration helps maintain consistency across measurements and prevents errors arising from inaccurate volumes.
Volumetric flasks: Volumetric flasks are designed to prepare aqueous solutions of precise volumes accurately with a calibration line on the neck. To calibrate a volumetric flask, it is important to fill it with distilled...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

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Related Experiment Video

Updated: Jun 16, 2026

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
07:13

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

Data fusion in multivariate calibration transfer.

Wangdong Ni1, Steven D Brown, Ruilin Man

  • 1School of Chemistry and Chemical Engineering, Central South University, Changsha, Hunan 410083, PR China.

Analytica Chimica Acta
|February 2, 2010
PubMed
Summary
This summary is machine-generated.

Stacked regression techniques significantly improve calibration transfer for multivariate models. Data fusion via stacking wavelet scales or spectral intervals enhanced predictive performance in near-infrared spectroscopy analysis.

Related Experiment Videos

Last Updated: Jun 16, 2026

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
07:13

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Spectroscopy

Background:

  • Multivariate calibration models are essential for spectroscopic data analysis.
  • Calibration transfer between instruments is crucial for practical applications.
  • Existing transfer methods have limitations in predictive performance.

Purpose of the Study:

  • To enhance predictive performance of transferred multivariate calibration models.
  • To evaluate the effectiveness of stacked regression techniques for calibration transfer.
  • To compare data fusion strategies with conventional transfer methods.

Main Methods:

  • Stacked Partial Least-Squares (SPLS) regression and stacked dual-domain regression analysis were employed.
  • Four calibration transfer techniques were investigated: piecewise direct standardization (PDS), orthogonal signal correction (OSC), model updating (MUP), and finite impulse response (FIR) filter.
  • Data fusion was achieved by stacking wavelet scales or spectral intervals.

Main Results:

  • Stacking significantly improved predictive performance for PDS, OSC, and MUP methods when transferring near-infrared spectral calibrations.
  • Data fusion by stacking wavelet scales or spectral intervals enhanced model performance.
  • While stacking alone showed less improvement for FIR filter transfer, applying SPLS regression to FIR-transferred spectra boosted predictive accuracy.

Conclusions:

  • Stacked regression techniques, combined with data fusion, offer a powerful approach to improve calibration transfer.
  • These methods enhance the reliability and applicability of multivariate models across different instruments.
  • The findings demonstrate a significant advancement in chemometric data analysis for spectroscopic applications.