Derivative Preprocessing and Optimal Corrections for Baseline Drift in Multivariate Calibration
By Christopher D. Brown, Lorenzo Vega-Montoto, Peter D. Wentzell. 2000
141 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Control and Systems Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/886367e4fd01a867db44de0447f6e61f1d67b75a
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In Analytical Chemistry
In Control and Systems Engineering
In Statistics and Probability
Citations in the corpus
- Chromatogram baseline estimation and denoising using sparsity (BEADS) (2014, methodology): “For instance in [5], the baseline drift is characterized as a “colored” noise, with a lowfrequency dominance in the noise power spectrum.”
- Application of Near-Infrared Spectroscopy for the Detection of Metanil Yellow in Turmeric Powder (2018, methodology): “Here in this work, the NIR spectra were subjected to 1st derivative, 2nd derivative (Brown et al. 2000), standard normal variate (SNV), multiplicative scatter correction (MSC) (Zeaiter and Roger 2005) pre-processing methods and their combinations to improve the result.”
- Data Pre-processing (2009, unknown): “This is done in order not to amplify high-frequency noise during the derivation process as is the case for fi nite difference derivation ( Brown et al. , 2000 ).”