1979
DOI: 10.1021/ac50044a010
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Application of recursive estimation to the real time analysis of trace metal analytes by linear sweep, pulse, and differential pulse anodic stripping voltammetry

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Cited by 12 publications
(4 citation statements)
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“…For a 5-mL sample containing both Cd2+ and Pb2+ in 0.01 M HN03 at these concentrations, the relative standard deviation (RSD) ranged from 2.5% to 5% (Cd) and from 8% to 15% (Pb), which is comparable to the results of others (7)(8)(9)(10)(11). Repetitive analysis (eight runs each of five determinations) of a standard 100 ppb solution of Cd2+ in 0.01 M HN03 gave a normal distribution, as indicated by the chi-square test (12).…”
Section: Resultssupporting
confidence: 81%
“…For a 5-mL sample containing both Cd2+ and Pb2+ in 0.01 M HN03 at these concentrations, the relative standard deviation (RSD) ranged from 2.5% to 5% (Cd) and from 8% to 15% (Pb), which is comparable to the results of others (7)(8)(9)(10)(11). Repetitive analysis (eight runs each of five determinations) of a standard 100 ppb solution of Cd2+ in 0.01 M HN03 gave a normal distribution, as indicated by the chi-square test (12).…”
Section: Resultssupporting
confidence: 81%
“…Barker and Brown have described an approach based on filtering the spectra obtained from a diode array detector, where the approach used was similar to that described in the section on spectroscopic applications. 5 5 , 5 6 The required model information consists of the spectra of the pure components; no assumptions about the chromatographic peak shape are required. 55 The accuracy of the approach can be improved by using pure component models measured at several different concentration levels.…”
Section: Chromatographymentioning
confidence: 99%
“…Seelig and Blount use the Kalman filter for recursive estimation of concentration values from anodic stripping voltammograms (134,135) and achieve greater precision them other methods used for ASV data analysis. Although their papers might well be considered as calibration papers as concentrations are the parameters in their model, the recursive Keilman filter is one of the most important tools of estimation theory.…”
Section: State and Parameter Estimationmentioning
confidence: 99%