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A hybrid optimization method for sample partitioning in near-infrared analysis.

Weihao Chen1, Huazhou Chen2, Quanxi Feng2

  • 1College of Science, Guilin University of Technology, Guilin 541004, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|November 25, 2020
PubMed
Summary

A new Adaptive Hybrid Cuckoo-Tabu Search (AHCTS) algorithm improves sample set partitioning for near-infrared (NIR) spectroscopy calibration models. This optimization enhances prediction accuracy in quantitative analysis compared to traditional methods.

Keywords:
Cuckoo searchFishmeal proteinHybrid optimizationNIR spectroscopySample partitioningTabu search

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Accurate calibration model prediction in quantitative analysis relies heavily on effective sample set division for spectroscopy.
  • Conventional methods like Kennard-Stone (KS) and Sample Set Partitioning based on Joint X-Y Distances (SPXY) use Euclidean distance for sample selection.
  • These existing algorithms may not always capture the most representative samples for robust model development.

Purpose of the Study:

  • To introduce an optimized sample partitioning algorithm for near-infrared (NIR) spectroscopy.
  • To enhance the prediction performance of calibration models in quantitative analysis.
  • To compare the proposed algorithm against established methods like KS and SPXY.

Main Methods:

  • Development of an Adaptive Hybrid Cuckoo-Tabu Search (AHCTS) algorithm for sample set optimization.
  • Integration of Cuckoo Search (CS) and Tabu Search (TS) algorithms with an adaptive function.
  • Application of KS, SPXY, and AHCTS for partitioning fishmeal spectral data to build Partial Least Squares Regression (PLSR) models.

Main Results:

  • The PLSR model developed using the AHCTS-partitioned sample set demonstrated superior performance compared to models built with KS and SPXY.
  • AHCTS effectively selected more representative samples, leading to improved prediction accuracy.
  • Experimental data from fishmeal samples validated the efficacy of the proposed method.

Conclusions:

  • The Adaptive Hybrid Cuckoo-Tabu Search (AHCTS) algorithm offers a significant advancement in sample set partitioning for NIR spectroscopy.
  • AHCTS provides a more advantageous alternative for developing accurate quantitative analysis models.
  • This optimized approach holds promise for various applications within NIR spectroscopy.