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Signal Smoothing with PLS Regression. / Panchuk, Vitaly; Semenov, Valentin; Legin, Andrey; Kirsanov, Dmitry.

In: Analytical Chemistry, Vol. 90, No. 9, 01.05.2018, p. 5959-5964.

Research output: Contribution to journal › Article › peer-review

Harvard

Panchuk, V, Semenov, V, Legin, A & Kirsanov, D 2018, 'Signal Smoothing with PLS Regression', Analytical Chemistry, vol. 90, no. 9, pp. 5959-5964. https://doi.org/10.1021/acs.analchem.8b01194

APA

Panchuk, V., Semenov, V., Legin, A., & Kirsanov, D. (2018). Signal Smoothing with PLS Regression. Analytical Chemistry, 90(9), 5959-5964. https://doi.org/10.1021/acs.analchem.8b01194

Vancouver

Panchuk V, Semenov V, Legin A, Kirsanov D. Signal Smoothing with PLS Regression. Analytical Chemistry. 2018 May 1;90(9):5959-5964. https://doi.org/10.1021/acs.analchem.8b01194

Author

Panchuk, Vitaly ; Semenov, Valentin ; Legin, Andrey ; Kirsanov, Dmitry. / Signal Smoothing with PLS Regression. In: Analytical Chemistry. 2018 ; Vol. 90, No. 9. pp. 5959-5964.

BibTeX

@article{ebe21f25e2fb45c29a41385fbecc2572,
title = "Signal Smoothing with PLS Regression",
abstract = "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.",
author = "Vitaly Panchuk and Valentin Semenov and Andrey Legin and Dmitry Kirsanov",
year = "2018",
month = may,
day = "1",
doi = "10.1021/acs.analchem.8b01194",
language = "English",
volume = "90",
pages = "5959--5964",
journal = "Industrial And Engineering Chemistry Analytical Edition",
issn = "0003-2700",
publisher = "American Chemical Society",
number = "9",

}

RIS

TY - JOUR

T1 - Signal Smoothing with PLS Regression

AU - Panchuk, Vitaly

AU - Semenov, Valentin

AU - Legin, Andrey

AU - Kirsanov, Dmitry

PY - 2018/5/1

Y1 - 2018/5/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85046400258&partnerID=8YFLogxK

U2 - 10.1021/acs.analchem.8b01194

DO - 10.1021/acs.analchem.8b01194

M3 - Article

AN - SCOPUS:85046400258

VL - 90

SP - 5959

EP - 5964

JO - Industrial And Engineering Chemistry Analytical Edition

JF - Industrial And Engineering Chemistry Analytical Edition

SN - 0003-2700

IS - 9

ER -

ID: 30504661