Gradient liquid chromatographic retention time prediction for suspect screening applications: A critical assessment of a generalised artificial neural network-based approach across 10 multi-residue reversed-phase analytical methods

Talanta

Volumn 147, Issue , 2016, Pages 261-270

  Barron, Leon P ^a   McEneff, Gillian L ^a  

^a KING'S COLLEGE LONDON   (United Kingdom)

Author keywords

Emerging contaminants; High resolution mass spectrometry; Non target analysis; Retention time prediction; Suspect screening

Indexed keywords

FORECASTING; MASS SPECTROMETRY; MULTILAYER NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; SCREENS (SIZING);

CHROMATOGRAPHIC RETENTION; CHROMATOGRAPHIC RETENTION TIME; EMERGING CONTAMINANT; GRADIENT REVERSED-PHASE LIQUID CHROMATOGRAPHY; HIGH RESOLUTION MASS SPECTROMETRY; MULTI-LAYER PERCEPTRONS; NON-TARGET ANALYSIS; RETENTION TIME PREDICTION;

LIQUID CHROMATOGRAPHY;

ARTIFICIAL NEURAL NETWORK; MASS SPECTROMETRY; PROCEDURES; REPRODUCIBILITY; REVERSED PHASE LIQUID CHROMATOGRAPHY;

CHROMATOGRAPHY, REVERSE-PHASE; MASS SPECTROMETRY; NEURAL NETWORKS (COMPUTER); REPRODUCIBILITY OF RESULTS;

EID: 84943564701     PISSN: 00399140     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.talanta.2015.09.065     Document Type: Article

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