Tikhonov regularization for spectra smoothing and baseline subtraction

Tikhonov regularization is is a very general method for signal smoothing, based on penalized least squares. This approach generalizes the ordinary least squares method by introducing a penalty term to ensure smoothness. The penalty term is tuneable, so that one ca use the same method for either smoothing or baseline extraction.

In this webinar, I’ll discuss the basics of the method, and show use cases for

• Smoothing of NIR spectra – simple case
• Smoothing of NIR spectra – general case of variable signal-to-noise ratio
• Baseline correction of Raman and XRF data

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