Adaptive Asymmetric Least Squares Baseline Estimation for Analytical Instruments.

Automated signal processing in analytical instrumentation is today required for the analysis of highly complex biomedical samples. Baseline estimation techniques are often used to correct long term instrument contamination or degradation. They are essential for accurate peak area integration. Some methods approach the baseline estimation iteratively, trying to ignore peaks which do not belong to the baseline. The proposed method in this work consists of a modification of the Asymmetric Least Squares (ALS) baseline removal technique developed by Eilers and Boelens. The ALS technique suffers from bias in the presence of intense peaks (in relation to the noise level). This is typical of diverse instrumental techniques such as Gas Chromatography-Mass Spectrometry (GC-MS) or Gas Chromatography-Ion Mobility Spectrometry (GC-IMS). In this work, we propose a modification (named psalsa) to the asymmetry weights of the original ALS method in order to better reject large peaks above the baseline. Our method will be compared to several versions of the ALS algorithm using synthetic and real GC signals. Results show that our proposal improves previous versions being more robust to parameter variations and providing more accurate peak areas.