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[Applied study on support vector machine (SVM) regression method in quantitative analysis with near-infrared

Lu-da Zhang1, Ze-chen Jin, Xiao-nan Shen

  • 1College of Science, China Argiculture University, Beijing 100094, China.

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|December 29, 2005
PubMed
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Support Vector Machine (SVM) regression accurately predicts wheat protein content using near-infrared (NIR) spectroscopy. This statistical method offers a reliable alternative to traditional chemical analysis for quantitative applications.

Area of Science:

  • Agricultural Science
  • Analytical Chemistry
  • Machine Learning

Context:

  • Quantitative analysis of agricultural products is crucial for quality control.
  • Near-infrared (NIR) spectroscopy offers a rapid, non-destructive analytical technique.
  • Traditional methods like Kjeldahl's are accurate but time-consuming.

Purpose:

  • To apply Support Vector Machine (SVM) regression for quantitative analysis of wheat protein content.
  • To evaluate the efficacy of SVM regression models with different kernel functions.
  • To compare SVM regression performance against Partial Least Squares (PLS) regression.

Summary:

  • Sixty-six wheat samples were analyzed using NIR spectroscopy.
  • SVM regression models were built using calibration samples and tested on predicting samples.

Related Experiment Videos

  • Models achieved high correlation coefficients (>0.97) and low average absolute error (<0.32) for protein content estimation.

    • Impact:

    • Demonstrates SVM regression as a viable and accurate method for quantitative analysis in NIR spectroscopy.
    • Highlights the potential for SVM regression in real-world applications for agricultural product analysis.
    • Provides a faster, potentially more cost-effective alternative to conventional protein analysis methods.