2016
DOI: 10.1556/1326.2016.28.3.2
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Simultaneous determination of thimerosal and aluminum in vaccines and pharmaceuticals with the use of HPLC method

Abstract: Summary.A simple and convenient chromatographic method of simultaneous separation, identification, and quantitative determination of thimerosal (TM) (preservative) and aluminum (adjuvant) in vaccines and pharmaceuticals by reversed phase highperformance liquid chromatography (RP-HPLC) with visible (VIS) detection was developed and validated. Due to postcolumn derivatization with dithizone, any interference from matrix was excluded. Similarly, a possibility of on-column decomposition of dithizonates was elimina… Show more

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Cited by 12 publications
(7 citation statements)
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“…It has been established that when the analyte concentration is above ∼10 À5 M, its response starts to saturate providing non-linear response. Quadratic curve-fitting calibration data are more appropriate [104,[109][110][111][112][113][114][115][116][117] than straight-line linear regression, in the case of some quantification methods. Matrix-related non-linearity is typical of methods such as LC-MS/MS.…”
Section: Linearity In Calibration Methods: Fluorescence Data As Examplementioning
confidence: 99%
“…It has been established that when the analyte concentration is above ∼10 À5 M, its response starts to saturate providing non-linear response. Quadratic curve-fitting calibration data are more appropriate [104,[109][110][111][112][113][114][115][116][117] than straight-line linear regression, in the case of some quantification methods. Matrix-related non-linearity is typical of methods such as LC-MS/MS.…”
Section: Linearity In Calibration Methods: Fluorescence Data As Examplementioning
confidence: 99%
“…The presented method can be applied to vaccines, pharmaceuticals, and other products containing TM and Al. 27…”
Section: Simultaneous Determination Of Thimerosal and Aluminum In Vaccines And Pharmaceuticals With The Use Of Hplc Methodmentioning
confidence: 99%
“…For Equation (12), r 2 coefficient is equal to 0.9998. Its value being so close to 1 suggests a nearly perfect regression equation.…”
Section: Least Squares Methods -Examplementioning
confidence: 99%