Signal Smoothing with PLS Regression.
| Autor: | Panchuk V; Institute of Chemistry , St. Petersburg State University , St. Petersburg , Russia 199034.; Laboratory of Artificial Sensory Systems , ITMO University , St. Petersburg , Russia 197101.; Institute for Analytical Instrumentation RAS , St. Petersburg , Russia 198095., Semenov V; Institute of Chemistry , St. Petersburg State University , St. Petersburg , Russia 199034.; Institute for Analytical Instrumentation RAS , St. Petersburg , Russia 198095., Legin A; Institute of Chemistry , St. Petersburg State University , St. Petersburg , Russia 199034.; Laboratory of Artificial Sensory Systems , ITMO University , St. Petersburg , Russia 197101., Kirsanov D; Institute of Chemistry , St. Petersburg State University , St. Petersburg , Russia 199034.; Laboratory of Artificial Sensory Systems , ITMO University , St. Petersburg , Russia 197101. |
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| Jazyk: | angličtina |
| Zdroj: | Analytical chemistry [Anal Chem] 2018 May 01; Vol. 90 (9), pp. 5959-5964. Date of Electronic Publication: 2018 Apr 10. |
| DOI: | 10.1021/acs.analchem.8b01194 |
| Abstrakt: | Smoothing of instrumental signals is an important prerequisite in data processing. Various smoothing methods were suggested through the last decades each having their own benefits and drawbacks. Most of the filtering methods are based on averaging in a certain window (e.g., Savitzky-Golay) or on frequency-domain representation (e.g., Fourier filtering). The present study introduces novel approach to signal filtering based on signal variance through PLS (projections on latent structures) regression. The influence of filtering parameters on the smoothed spectrum is explained and real world examples are shown. |
| Databáze: | MEDLINE |
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