Sparse methods in spectroscopy: an introduction, overview, and perspective.

Area of Science:

• Chemometrics
• Spectroscopy
• Statistical Learning

Background:

• Multivariate calibration methods like partial least-squares often create complex, non-parsimonious models.
• High-dimensional data and large sample sizes necessitate model simplification for interpretability.

Purpose of the Study:

• To review regression problems in sparse modeling for spectroscopic applications.
• To adapt sparse modeling algorithms for both wavelength and sample selection.
• To highlight user-friendly sparse modeling techniques for spectroscopists.

Main Methods:

• Review of sparse regression problems and their spectroscopic relevance.
• Application of sparse modeling algorithms for wavelength selection.
• Re-appropriation of sparse modeling for sample selection.
• Focus on interpretable, non-black-box algorithms.

Main Results:

• Sparse modeling effectively reduces calibration model complexity by utilizing zero-valued regression coefficients.
• Demonstrated applicability of sparse modeling to three diverse spectroscopic datasets.
• Successful adaptation of wavelength selection algorithms for sample selection.

Conclusions:

• Sparse modeling offers a powerful and interpretable approach to multivariate calibration in spectroscopy.
• User-friendly sparse modeling algorithms can enhance variable and sample selection processes.
• These methods provide a valuable alternative to complex "black-box" statistical learning models.