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A two-dimensional sample screening method based on data quality and variable correlation.

Gang Li1, Dan Wang1, Kang Wang1

  • 1State Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin, 300072, China; China and Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin, 300072, China.

Analytica Chimica Acta
|April 1, 2022
PubMed
Summary
This summary is machine-generated.

Selecting high-quality spectral data is crucial for accurate modeling. A new two-dimensional sample selection (TDSS) method improves model accuracy and robustness in spectral analysis.

Keywords:
Dynamic spectrumMahalanobis distancePLSSample screeningSpectral analysisTraining set

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Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Spectroscopy

Background:

  • Spectral data quality is vital for reliable model development.
  • Interference factors can introduce deviations in spectral data, biasing models.
  • Effective sample selection is necessary to obtain representative datasets.

Purpose of the Study:

  • To introduce a novel two-dimensional sample selection (TDSS) method.
  • To compare TDSS with the Mahalanobis distance method for sample screening.
  • To enhance the accuracy and prediction performance of spectral models.

Main Methods:

  • Developed a two-dimensional sample selection (TDSS) method considering data quality and variable correlation.
  • Applied TDSS and Mahalanobis distance method to dynamic spectrum (DS) data.
  • Built partial least squares (PLS) linear regression models with quadratic nonlinear correction.

Main Results:

  • The TDSS method significantly improved model accuracy and prediction performance.
  • TDSS outperformed the Mahalanobis distance method in sample screening.
  • Correlation coefficients above 0.82 were achieved for predicting triglyceride and total cholesterol.

Conclusions:

  • The proposed TDSS method is effective for sample selection in spectral analysis.
  • TDSS enhances model accuracy and robustness for complex solutions.
  • This study offers a new approach for selecting modeling sets in spectral analysis.