Optimization and quantification of the systematic effects of a rolling circle filter for spectral pre-processing

Abstract

The presented method allows us to quantify and correct the systematic influence of a rolling circle filter for quantitative spectroscopic measurements.

An adaptive extended Gaussian peak derivative reweighted penalised least squares method for baseline correction

“…agdPLS has three main improvements. First, based on the logistic function in the weight-updating strategy of eqn (11), the derivative term of the difference between the spectra and baseline is added to consider the rate of change of the error. Second, the baseline-estimation eqn ( 9) is improved by introducing a peak penalty factor and adjusting the penalty coefficient l adaptively.…”

“…Its weights-update strategy is: when the spectrum is smaller than the tted baseline, the weights are set to 1. When the spectrum is greater than or equal to the tted baseline, a logistic function is used to update the weights and the weights-update strategy is shown in eqn (11).…”

“…Simultaneously, the simulated and actual data provided in the research included only single and double peaks, and did not consider the case of more peaks overlapping. For more complex spectra and baselines, according to the weight-update strategy shown in eqn (11), the weights of both the baseline regions and peak regions are close to 1, which can lead to severe overtting and undertting.…”

“…10 BC is essential for pre-processing spectra, especially in long-term online monitoring. 11 In recent years, several research results in BC have been presented. [12][13][14] Common BC methods include polynomial tting, 15 segmented tting, 16 derivative spectroscopy, 17 wavelet transformation, 18 moving-window averaging, 19 robust baseline estimation, 20 penalised least squares (PLS), 21 morphological operators, 22 sparse representation, 23 and deep learning.…”

Quantitative Long-Term Monitoring of the Circulating Gases in the KATRIN Experiment Using Raman Spectroscopy

“…These binned spectral data sets are then treated for a variety of effects, to improve the quality of the final Raman spectrum. These include, as described in reference [ 30 ], (i) the removal of cosmic rays (to avoid potential false feature identification; note that for this procedure at least two subsequent Raman data sets are required); (ii) spectral intensity correction (utilizing a 2D calibration map generated from supplemental SRM2242 measurements); (iii) spectrometer astigmatism correction (shifting the aforementioned binned chip segments to line up in the non-dispersive direction); (iv) summing up all bin segments (to generate the total-signal Raman spectrum); and (v) the removal of any background (mostly fluorescence induced by the excitation laser in optical elements)—note that the removal procedure is based on the rolling-circle filter methodology (see, e.g., [ 39 , 40 ]).…”