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Related Concept Videos

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...
IR Spectrometers01:25

IR Spectrometers

There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...
UV–Vis Spectrometers01:14

UV–Vis Spectrometers

The absorbance of UV and visible (UV–visible) radiations is measured using a UV–visible spectrophotometer. Deuterium lamps, which emit UV radiation, and tungsten lamps, which produce radiation in the visible region, are used as light sources in UV–visible spectrophotometers. A monochromator or prism is used for diffraction grating, i.e., to split the incoming radiation into different wavelengths. A system of slits is used to focus the desired wavelength on the sample cell. Samples for...
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...
Spectrophotometry: Introduction01:16

Spectrophotometry: Introduction

Spectrophotometry is the quantitative measurement of the absorption, reflection, diffraction, or transmission of electromagnetic radiation through a material as a function of the intensity and wavelength of the radiation. A spectrophotometer is a device used to measure the change in the radiation intensity caused by its interaction with the material.
The essential components of a spectrophotometer include a source of electromagnetic radiation, a slot for placing a material to be analyzed, and a...
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...

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[Calibration transfer between two FTNIR spectrophotometers using SVR].

Long-lian Zhao1, Jun-hui Li, Wen-juan Zhang

  • 1College of Information and Electrical Engineering, China Agricultural University, Beijing 100094, China.

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|January 7, 2009
PubMed
Summary
This summary is machine-generated.

This study successfully transferred a maize protein calibration model between two Fourier Transform Near-Infrared (FTNIR) spectrophotometers using a moving window Support Vector Regression (SVR) method. The SVR approach effectively corrected instrument differences, enabling accurate predictions on the secondary instrument.

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Fourier Transform Near-Infrared (FTNIR) spectroscopy is valuable for analyzing agricultural products like maize.
  • Calibration transfer between different FTNIR instruments is challenging due to variations in spectral data.
  • Accurate calibration transfer is crucial for widespread adoption and reliable application of FTNIR models.

Purpose of the Study:

  • To investigate the calibration transfer of a maize protein model between two FTNIR spectrophotometers.
  • To develop and validate a method for correcting spectral differences between instruments.
  • To assess the effectiveness of Support Vector Regression (SVR) for calibration transfer.

Main Methods:

  • Utilized a moving window Support Vector Regression (SVR) approach to establish a transformation matrix.
  • Built a maize protein calibration model on a "master" Bruker Vector 22/N instrument.
  • Applied the SVR model to spectra from a "slave" Bruker MPA instrument to correct for differences.

Main Results:

  • Achieved a good transfer performance with a correlation coefficient (r) of 0.9434.
  • Obtained a relative standard deviation (RSD) of 4.23% after calibration transfer.
  • Demonstrated that transformed spectra from the "slave" instrument closely matched those from the "master".

Conclusions:

  • The moving window SVR method is effective for successful calibration transfer between FTNIR spectrophotometers.
  • This technique allows a calibration model developed on one instrument to be reliably used on another.
  • The findings support the broader application of FTNIR spectroscopy in agricultural analysis by overcoming instrument-specific limitations.