Support vector regression for functional data in multivariate calibration problems.

Area of Science:

• Chemometrics
• Spectroscopy
• Data Analysis

Background:

• Multivariate calibration commonly treats spectral data as discrete points.
• Functional data analysis offers an alternative approach by modeling spectra as functions.
• Existing regression methods for functional data have limitations in chemometric applications.

Purpose of the Study:

• To propose and evaluate functional data analysis support vector regression (FDA-SVR) for multivariate calibration.
• To assess FDA-SVR's performance in solving linear and nonlinear calibration problems.
• To compare FDA-SVR against traditional chemometric calibration methods.

Main Methods:

• Application of support vector regression for functional data (FDA-SVR).
• Analysis of three distinct spectral datasets (chromatograms, NIR, MIR).
• Comparative performance evaluation against Partial Least Squares (PLS), Support Vector Regression (SVR), and Least Squares Support Vector Regression (LS-SVR).

Main Results:

• FDA-SVR demonstrated satisfactory performance in multivariate calibration tasks.
• The method proved effective for both linear and nonlinear calibration problems.
• Comparative analysis indicated FDA-SVR's potential as a valuable tool.

Conclusions:

• FDA-SVR is an effective and promising technique for multivariate calibration.
• The functional data approach offers advantages for spectral data analysis.
• FDA-SVR provides a robust alternative to traditional chemometric methods.