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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.
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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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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

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Published on: September 7, 2019

Blank augmentation protocol for improving the robustness of multivariate calibrations.

Kirsten E Kramer1, Gary W Small

  • 1Optical Science and Technology Center and Department of Chemistry, University of Iowa, Iowa City, IA 52242, USA.

Applied Spectroscopy
|June 9, 2007
PubMed
Summary

This study introduces a robust method to update near-infrared spectroscopy calibration models using blank samples. This technique significantly improves glucose prediction accuracy in simulated blood plasma over time.

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Multivariate calibration models, particularly those using near-infrared (NIR) spectroscopy, are susceptible to instrumental drift over time.
  • Maintaining model accuracy requires periodic recalibration or updating procedures.
  • Robust methods are needed to adapt existing models to changing instrumental conditions without extensive recalibration.

Purpose of the Study:

  • To develop and evaluate an updating procedure for enhancing the robustness of multivariate calibration models in NIR spectroscopy.
  • To assess the effectiveness of using spectra from a single blank sample to correct for instrumental drift.
  • To improve the long-term prediction accuracy of glucose concentration in a simulated biological matrix.

Main Methods:

  • Acquisition of repeated spectra from a single blank sample during instrumental warm-up to capture daily instrumental profiles.
  • Augmentation of original calibration spectra with blank sample spectra to create an updated calibration dataset.
  • Application of partial least squares (PLS) regression for multivariate calibration and updating.
  • Evaluation of the updated model's performance using prediction samples acquired over an extended period (approx. six months).

Main Results:

  • The updating procedure effectively incorporated instrumental drift, significantly improving model robustness.
  • An augmented PLS model achieved a standard error of prediction (SEP) of 0.79 mM for glucose (1-20 mM range) when applied to data collected 176 days post-calibration.
  • Without model updating, the original PLS model showed a severely degraded SEP of 13.4 mM, highlighting the necessity of the proposed method.

Conclusions:

  • The described updating procedure using blank sample spectra is a highly effective strategy for maintaining the accuracy of NIR spectroscopic calibration models.
  • This method offers a practical and robust solution for long-term glucose monitoring in simulated biological matrices, overcoming challenges posed by instrumental variability.
  • The protocol significantly enhances the reliability and longevity of multivariate calibration models in real-world analytical applications.