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Method for screening near-infrared quantitative models with high robustness.

Zhuopin Xu1, Xiaohong Li2, Zhiyi Zhang2

  • 1Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China.

Analytica Chimica Acta
|July 3, 2025
PubMed
Summary
This summary is machine-generated.

A new method called external calibration-assisted screening (ECA) improves the robustness of near-infrared (NIR) quantitative models. This approach helps select models that perform reliably across different measurement conditions, reducing the need for frequent recalibration.

Keywords:
Competitive adaptive reweighting samplingExternal calibrationModel robustnessNear-infrared spectroscopyVariable selection

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Area of Science:

  • Chemometrics
  • Spectroscopy
  • Analytical Chemistry

Background:

  • Near-infrared (NIR) calibration models often show sensitivity to changing measurement conditions.
  • Existing algorithms prioritize accuracy, but robustness is crucial for sustained model performance and minimizing recalibration.
  • A more robust model, even if slightly less accurate, can be more practical for real-world applications.

Purpose of the Study:

  • To develop a simple method, external calibration-assisted screening (ECA), for identifying the most robust quantitative models.
  • To introduce a new metric, PrRMSE, for assessing model robustness using cross-validation and external calibration.
  • To integrate ECA with competitive adaptive reweighted sampling (CARS) for enhanced model optimization.

Main Methods:

  • The external calibration-assisted screening (ECA) method involves externally calibrating a previously developed model with samples from new conditions.
  • A new metric, PrRMSE, is introduced to quantify model robustness based on cross-validation and external calibration results.
  • The ECA method was integrated with competitive adaptive reweighted sampling (CARS), creating the ECCARS approach, and tested on rice flour and corn datasets.

Main Results:

  • The ECCARS method demonstrated significantly enhanced model robustness compared to the standard CARS method across different datasets.
  • Models selected by ECCARS showed substantial reductions in root mean square error (RMSE) for both calibration (12.15%-725%) and validation (27.63%-482.00%) under varying conditions.
  • The results indicate a marked improvement in the reliability of NIR quantitative models when using the proposed ECCARS method.

Conclusions:

  • The developed ECA method provides a straightforward approach to evaluate and select robust NIR quantitative models.
  • The ECCARS method offers a practical solution for improving the stability and reliability of NIR models in diverse analytical scenarios.
  • This approach has the potential to lower barriers to near-infrared spectroscopy (NIRS) adoption and increase its overall effectiveness.