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A correlation-analysis-based wavelength selection method for calibration transfer.

Zhuopin Xu1, Shuang Fan1, Weimin Cheng1

  • 1Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, 350 Shushanhu Road, Hefei, Anhui 230031, People's Republic of China; University of Science and Technology of China, No. 96 Jinzhai Road, Hefei, Anhui 230026, People's Republic of China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|January 28, 2020
PubMed
Summary
This summary is machine-generated.

Calibration transfer for near-infrared spectroscopy (NIRS) is improved by selecting wavelengths with correlated spectral signals between instruments. This correlation-analysis-based wavelength selection method (CAWS) enhances model prediction accuracy.

Keywords:
Calibration transferNear-infrared spectroscopyWavelength selection

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Spectral signal variations across different near-infrared spectroscopy (NIRS) instruments necessitate calibration transfer for widespread application.
  • Effective calibration transfer requires selecting spectral variables that exhibit stable inter-instrument signals and contain relevant component information.

Purpose of the Study:

  • To propose and evaluate a novel correlation-analysis-based wavelength selection method (CAWS) for improving NIRS calibration transfer.
  • To assess the performance of CAWS against existing wavelength selection techniques using corn and rice bran datasets.

Main Methods:

  • Developed a correlation-analysis-based wavelength selection (CAWS) method focusing on wavelengths with high Pearson's correlation coefficient (|Ri|) between master and slave instruments.
  • Applied CAWS to NIRS datasets for corn and rice bran, comparing its effectiveness with other wavelength selection methods.
  • Investigated the impact of various pretreatment methods and calibration transfer algorithms on CAWS performance.

Main Results:

  • CAWS demonstrated superior calibration transfer performance, achieving lower root mean square errors of prediction (RMSEPtrans).
  • The method effectively identified stable and informative spectral variables for inter-instrument calibration transfer.
  • Pretreatment methods and calibration transfer algorithms influenced the overall efficiency of the CAWS-optimized models.

Conclusions:

  • The proposed CAWS method significantly enhances the efficiency of NIRS calibration transfer by selecting robust spectral variables.
  • CAWS offers a promising approach for standardizing NIRS data across different instruments.
  • Further research combining CAWS with advanced algorithms could yield even greater improvements in calibration transfer accuracy.