2001
DOI: 10.1002/1521-4109(200108)13:12<1041::aid-elan1041>3.0.co;2-z
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Resolution of Voltammetric Peaks Using Chemometric Multivariate Calibration Methods

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Cited by 16 publications
(3 citation statements)
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“…This method involves the factor decomposition of the experimental data, such as voltammetric data in this case, into a reduced set of latent variables (components or factors). These latent variables are linear combinations of original variables that correlate optimally with the variables to be predicted 37. In our particular case, the purpose of the PLS regression method was to build up a prediction model for the concentration of the two analytes (Pb and Cd) in unknown samples using efficiently the information contained in the voltammograms and the concentration of the analytes in the calibration samples.…”
Section: Chemometrics Methodologiesmentioning
confidence: 99%
See 1 more Smart Citation
“…This method involves the factor decomposition of the experimental data, such as voltammetric data in this case, into a reduced set of latent variables (components or factors). These latent variables are linear combinations of original variables that correlate optimally with the variables to be predicted 37. In our particular case, the purpose of the PLS regression method was to build up a prediction model for the concentration of the two analytes (Pb and Cd) in unknown samples using efficiently the information contained in the voltammograms and the concentration of the analytes in the calibration samples.…”
Section: Chemometrics Methodologiesmentioning
confidence: 99%
“…Thus, in this work the possibility to extract qualitative and quantitative analytical information through the application of MCR‐ALS to unresolved voltammetric peaks of Pb and Cd recorded using a vibrating gold microwire electrode is explored. Since other multivariate calibration methods, particularly based on partial least squares (PLS) regression, are also frequently used to extract analytical information from spectrometric and also voltammetric signals 15, 36, 37, the results obtained by the MCR‐ALS method were compared with those achieved by the PLS and classical regression (CLS) methods.…”
Section: Introductionmentioning
confidence: 99%
“…a n a l y t i c a c h i m i c a a c t a 6 2 6 ( 2 0 0 8 )[130][131][132][133][134][135][136][137][138][139][140][141][142][143][144][145][146] …”
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