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[Improving partial least square regression precision in NIR multi-component analysis using artificial neural

Ying-kui Bai1, Xian-jiang Meng, Dong Ding

  • 1Jilin University, College of Communication Engineering, Changchun 130025, China.

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|July 15, 2005
PubMed
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This study introduces a novel near-infrared (NIR) multi-component analysis method combining Artificial Neural Network (ANN) and Partial Least Square Regression (PLS). The new approach significantly improves prediction accuracy for complex samples compared to traditional PLS methods.

Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Near-infrared (NIR) spectroscopy is widely used for multi-component analysis.
  • Traditional Partial Least Square Regression (PLS) models can face limitations in applicability and precision with complex samples.
  • Accurate quantification of multiple components in a sample is crucial across various scientific fields.

Purpose of the Study:

  • To develop and validate a new NIR multi-component analysis method.
  • To enhance the prediction precision and model applicability compared to conventional PLS.
  • To integrate Artificial Neural Network (ANN) with PLS for improved analytical performance.

Main Methods:

  • A novel method combining Artificial Neural Network (ANN) and Partial Least Square Regression (PLS) for NIR multi-component analysis.

Related Experiment Videos

  • Division of training sample concentration ranges into sub-ranges.
  • Computation of PLS correlation models for each sub-range.
  • Classification of prediction samples into sub-ranges using ANN.
  • Prediction of component concentration using the corresponding sub-range PLS model.

    • Main Results:

    • The proposed method demonstrates improved model applicability.
    • A significant enhancement in prediction precision was observed compared to traditional PLS.
    • The integration of ANN for sample classification within sub-ranges led to superior results.

    Conclusions:

    • The developed ANN-PLS hybrid method offers a more robust and precise approach for NIR multi-component analysis.
    • This technique effectively addresses the limitations of traditional PLS in handling complex samples.
    • The findings suggest a promising advancement in chemometric analysis for quantitative spectroscopy.