Machine learning tools to estimate the severity of matrix effects and predict analyte recovery in inductively coupled plasma optical emission spectrometry.

Carter, Jake A; O'Brien, Logan M; Harville, Tina; Jones, Bradley T; Donati, George L

Carter, Jake A; O'Brien, Logan M; Harville, Tina; Jones, Bradley T; Donati, George L.

Afiliação

Carter JA; Department of Chemistry, Wake Forest University, Salem Hall, Box 7486, Winston-Salem, NC, 27109, USA.
O'Brien LM; Department of Chemistry, Wake Forest University, Salem Hall, Box 7486, Winston-Salem, NC, 27109, USA.
Harville T; Agilent Technologies, 2500 Regency Parkway, Cary, NC, 27518, USA.
Jones BT; Department of Chemistry, Wake Forest University, Salem Hall, Box 7486, Winston-Salem, NC, 27109, USA.
Donati GL; Department of Chemistry, Wake Forest University, Salem Hall, Box 7486, Winston-Salem, NC, 27109, USA. Electronic address: donatigl@wfu.edu.

Talanta ; 223(Pt 2): 121665, 2021 Feb 01.

Article em En | MEDLINE | ID: mdl-33298255

Palavras-chave

Accuracy prediction; Easily ionizable elements; Matrix effects; Plasma naturally-occurring species; Supervised machine learning; Unsupervised machine learning

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Talanta Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos

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