Baselinekorrigering

Baselinekorrigering is a data-processing technique used to remove baseline drift, offset, and slowly varying background from signals such as UV-Vis spectra, chromatograms, or mass spectra. The aim is to reveal true signal features like peaks and to enable accurate quantification. It is common in analytical chemistry, spectroscopy, and related fields, where instrumental or sample-related backgrounds can obscure signals.

Methods range from simple subtracting a constant baseline to more sophisticated models. Constant or linear baseline removal assumes a flat or straight background. Polynomial fitting uses a low-order polynomial fitted to presumed baseline regions and subtracted. Robus variants include iterative exclusion of peak regions. More advanced methods include penalized least squares (Whittaker) and asymmetric least squares (ALS) baseline correction, sometimes with adaptive schemes such as airPLS. 2D baselines or images can use rolling-ball or similar approaches.

Typical workflow: identify baseline regions, select a model, fit the baseline, subtract, and validate by inspecting residuals and peak shapes; adjust parameters as needed. Preprocessing steps such as smoothing or normalization may accompany baseline correction. Good baseline correction improves peak areas, heights and times-to-peak, and enhances comparability across samples.

Limitations include risk of removing parts of genuine signal if the baseline model is too flexible, or leaving residual background if too rigid. The choice of baseline regions and model parameters is critical and may require dataset-specific tuning. Baseline correction is a routine step in many analytical pipelines and is often documented in instrument methods and data-processing protocols.