Here's a bit of technical content from me - today a deep dive on #baseline correction methods.
Baseline correction is a preprocessing technique to remove background signal and isolate peaks in hashtag#spectroscopy data.
In my recent post I discuss two methods:
1. Wavelet transform (WT) - Decomposes signal into components at different frequencies. Lowest frequency component represents baseline and can be removed.
2. Asymmetric least squares (ALS) - Fits a smooth baseline function, penalising positive deviations more than negative ones.
TL;DR: WT method is intuitive but can distort peaks. ALS produces better results.
Both methods are applied on a #Raman spectrum and an X-ray fluorescence (#XRF) spectrum. ALS gives a cleaner baseline correction and it's effective for removing broad, slowly varying background while preserving sharper spectral features.
#chemometrics #Python #MachineLearning #wavelets #regression
https://nirpyresearch.com/two-methods-baseline-correction-spectral-data/