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Local Strategy Combined with a Wavelength Selection Method for Multivariate Calibration.

Haitao Chang1, Lianqing Zhu2, Xiaoping Lou3

  • 1School of Instrumentation Science & Opto-Electronics Engineering, Beihang University, Beijing 100191, China. changhaitao2005@126.com.

Sensors (Basel, Switzerland)
|June 9, 2016
PubMed
Summary

This study introduces a new method for building accurate multivariate calibration models using local regression and wavelength selection. The approach improves prediction accuracy and simplifies complex models for analyzing food dye concentrations.

Keywords:
local algorithmmultivariate calibrationpartial least squares regressionultraviolet-visible absorbance spectrawavelength selection

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Area of Science:

  • Analytical Chemistry
  • Chemometrics

Background:

  • Accurate multivariate calibration models are crucial for quantitative analysis.
  • Calibration data quality significantly impacts prediction accuracy.
  • Existing methods may struggle with complex spectral data and noise.

Purpose of the Study:

  • To develop an improved method for constructing multivariate calibration models.
  • To enhance prediction accuracy and reduce model complexity.
  • To address challenges posed by noisy spectral data and sample similarity.

Main Methods:

  • A local regression strategy was employed to select calibration samples similar to unknown samples.
  • Synthetic degree of grey relation coefficient was used for spectral similarity evaluation.
  • Wavelength selection based on interactive self-modeling mixture analysis identified informative variables.

Main Results:

  • The proposed method successfully enhanced the prediction accuracy of partial least squares regression models.
  • Model complexity was significantly reduced by selecting relevant wavelengths and similar samples.
  • Effective analysis of ultraviolet-visible absorbance spectra for food dye mixtures was demonstrated.

Conclusions:

  • The combined local regression and wavelength selection approach offers a robust strategy for multivariate calibration.
  • This method improves analytical performance and model interpretability.
  • It provides a valuable tool for quantitative spectral analysis in various applications.