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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.
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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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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...
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AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...

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

Published on: September 7, 2019

Spectral multivariate calibration without laboratory prepared or determined reference analyte values.

Josh Ottaway1, Jeremy A Farrell, John H Kalivas

  • 1Department of Chemistry, Idaho State University, Pocatello, Idaho 83209, United States.

Analytical Chemistry
|January 1, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces pure component Tikhonov regularization (PCTR), a novel calibration method that eliminates the need for costly laboratory-prepared reference samples. PCTR uses analyte pure component spectra and nonanalyte spectra for accurate predictions in visible and near-infrared (NIR) spectroscopy.

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Area of Science:

  • Spectroscopy
  • Chemometrics
  • Analytical Chemistry

Background:

  • Calibration is essential for accurate spectral analysis.
  • Traditional calibration requires time-consuming and expensive reference samples.
  • Existing methods often struggle with complex sample matrices and varying measurement conditions.

Purpose of the Study:

  • To introduce a new calibration method, pure component Tikhonov regularization (PCTR).
  • To reduce the cost and time associated with spectral calibration.
  • To provide an alternative calibration approach that does not rely on laboratory-prepared reference values.

Main Methods:

  • Developed the pure component Tikhonov regularization (PCTR) method.
  • Utilized analyte pure component spectra and nonanalyte spectra for calibration.
  • Applied PCTR to visible and near-infrared (NIR) spectral data sets.

Main Results:

  • PCTR achieved comparable results to ridge regression (RR) using reference calibration sets.
  • The method demonstrated accuracy in predictions without requiring determined reference values.
  • PCTR showed flexibility in calibration maintenance under changing measurement conditions.

Conclusions:

  • PCTR offers a cost-effective and efficient alternative for spectral calibration.
  • The method is robust and adaptable to various spectral data and conditions.
  • PCTR provides accurate predictions by balancing model shrinkage and orthogonality to nonanalyte content.