Analytical and Bioanalytical Chemistry

, Volume 392, Issue 3, pp 515–521 | Cite as

An ensemble method based on a self-organizing map for near-infrared spectral calibration of complex beverage samples

Original Paper

Abstract

Based on a so-called ensemble strategy, an algorithm is proposed for near-infrared (NIR) spectral calibration of complex beverage samples. This algorithm is a combination of a novel training set/test set sample-selection procedure based on a Kohonen self-organizing map (SOM) with a simple procedure to calculate an average partial least-squares (PLS) calibration model, which is therefore named SOMEPLS. In order to verify the proposed SOMEPLS, two NIR beverage datasets involving the determination of sugar content are considered, and three kinds of reference algorithm, i.e., conventional PLS (CPLS), the Kennard-Stone (KS) algorithm in combination with PLS (KSPLS), and sample set partitioning based on the joint x-y distance (SPXY) algorithm in combination with PLS (SPXYPLS), are used. Of these, both KS and SPXY are well-known representative sample-selection algorithms. By comparison, it was found that when there is a training set of appropriate size, SOMEPLS can achieve better prediction accuracy than the three reference algorithms, but without increasing the complexity of the corresponding calibration model for the future application, indicating that SOMEPLS can serve as a promising tool for NIR spectral calibration.

Keywords

Near-infrared Calibration Accuracy Ensemble Self-organizing map Sugar concentration 

Notes

Acknowledgements

This work was supported by Scientific Research Fund of Sichuan Provincial Education Department of China

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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Department of Chemistry and Chemical EngineeringYibin UniversityYibinChina
  2. 2.College of ChemistrySichuan UniversityChengduChina

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