Adaptive Asymmetric Least Squares baseline estimation for analytical instruments

Abstract

Automated signal processing in analytical instru mentation 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 GasChromatography-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.

Classification of Bitter Orange Essential Oils According to Fruit Ripening Stage by Untargeted Chemical Profiling and Machine Learning

“…The next step was to correct the baseline of the chromatograms. This was carried out using the algorithm named ‘ psalsa ’ ( Figure 1 c), previously developed by the Reference [ 38 ]. This algorithm is a modification of the asymmetric least squares (ALS) baseline removal technique proposed by Eilers and Boelens [ 39 ].…”

“…This algorithm is a modification of the asymmetric least squares (ALS) baseline removal technique proposed by Eilers and Boelens [ 39 ]. Briefly, an adaptive value for the weights of the ALS method depending on the residuals was implemented in order to be more robust in term of parameter variations and to provide more accurate peak intensities [ 38 ].…”

Asymmetric least‐squares baseline algorithm with peak screening for automatic processing of the Raman spectra

“…The automatic baseline detection algorithms are being actively developed for the past decade. State‐of‐the‐art approaches include mathematical morphology, [ 1,2 ] ALS based, [ 3,4 ] and wavelet transform. [ 4,5 ]…”

“…One of the problems of the ALS approach is that under the peaks, the values may become large enough to cause the inflections of the estimated baseline z. To improve the performance of the ALS approach, several modifications of the algorithm have been proposed (see previous works [ 3,4 ] and references therein). The most successful was an idea to multiply the weights p by for y i > z i , where k is proportional to the characteristic peak height.…”

“…The most successful was an idea to multiply the weights p by for y i > z i , where k is proportional to the characteristic peak height. [ 3 ] In this way, the peak influence is penalized by the amplitude. The aim of the present work is to propose a more efficient peak screening algorithm, which can quantitatively improve the baseline estimation.…”