pp_asls — Asymmetric least-squares baseline correction (AsLS)¶
Group: Baseline correction · C ABI: n4m_transform_asls_*
Description¶
Asymmetric Least Squares (Eilers & Boelens 2005).
Parameters¶
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API and bindings¶
C ABI (ABI 2): n4m_transform_asls_create · n4m_transform_asls_destroy · n4m_transform_asls_transform. Use the linked public header for the exact signature, configuration, and result handles.
Python (verified public re-export):
from n4m.transform.baseline import AsLS
Source signature: AsLS(lam: float = 1000000.0, p: float = 0.01, max_iter: int = 50, tol: float = 0.001).
R: no current source-verified entry point was found for this catalog method.
MATLAB / Octave: no current source-verified entry point was found for this catalog method.
Explanations¶
Bibliographic source¶
Eilers & Boelens (2005), Baseline correction with asymmetric least squares smoothing, Leiden University Medical Centre technical report; algorithmic source also summarized at https://doi.org/10.1039/C4AN01061B.
Mathematical principle¶
AsLS alternates minimization of \(\sum_j w_j(y_j-z_j)^2+\lambda\|D^2z\|^2\) with \(w_j=p\) for positive residuals and \(w_j=1-p\) otherwise. Subtracting the smooth \(z\) preserves narrow peaks while suppressing background curvature.
Appropriate uses¶
Baseline correction for Raman/NIR-like traces when the sign of analyte peaks is known and a controllable smoothness/asymmetry trade-off is wanted.
Limits and validation¶
Both \(\lambda\) and \(p\) require tuning; broad peaks may enter the baseline and the binary residual weighting can converge slowly near ambiguous points.
Implementation¶
n4m.transform.baseline.AsLS wraps n4m_transform_asls_*; the Whittaker system and weight updates live in preprocessing/baselines/asls.c.
The ABI-2 implementation is the n4m_transform_asls_* lifecycle in libn4m.
Sources and provenance¶
https://github.com/GBeurier/nirs4all-methods/blob/main/cpp/src/core/preprocessing/baselines/asls.c; https://doi.org/10.1039/C4AN01061B
See also: methods index · interactive dashboard