Baseline Removal

Table of contents

  1. Introduction
  2. Behavior

Introduction

The Baseline Removal parent page groups algorithms that estimate and subtract broad background trends from spectra. SpectraGuru includes AirPLS for adaptive reweighted smoothing, ModPoly for polynomial baseline fitting, Gaussian-Lorentzian fitting for range-based curve fitting, SNIP for iterative peak clipping, and ALS for asymmetric penalized least-squares smoothing.

Behavior

This feature should identify and remove unwanted baselines from spectral data using one selected algorithm:

  • AirPLS: Iteratively adjusts the baseline using a penalized least squares approach to smooth the background while preserving peaks.
  • ModPoly: Fits a polynomial function to estimate and subtract the baseline from the spectra.
  • Gaussian-Lorentzian fitting: Uses a Gaussian-Lorentzian hybrid to fit the data and find a baseline.
  • SNIP: Iteratively clips peaks from a transformed spectrum to estimate background.
  • ALS: Fits a smooth asymmetric least-squares baseline below peaks.

Table of contents