Calibration curve

About: Calibration curve is a research topic. Over the lifetime, 6552 publications have been published within this topic receiving 95128 citations.

High-performance liquid chromatography method for determination of carnosic acid in rat plasma and its application to pharmacokinetic study

Abstract: A sensitive and reproducible high-performance liquid chromatography method with ultraviolet detection (UV) was developed for the determination of carnosic acid (CA) in rat plasma. After simple acidification and liquid-liquid extraction of plasma samples using gemfibrozil as an internal standard, the supernatant was evaporated to dryness under a gentle stream of nitrogen. The residue was reconstituted in 200 microL before being injected into the chromatographic system. The analysis was performed on a C(18) column protected by an ODS guard column using acetonitrile-0.1% phosphoric acid (55:45, v/v) as mobile phase, and the wavelength of the UV detector was set at 210 nm. The calibration curve was linear over the range of 0.265-265.0 microg/mL with a correlation coefficient of 0.9997. The recovery for plasma samples of 0.530, 13.25, 132.5 and 265.0 microg/mL was 72.2, 87.9, 90.4 and 94.7%, respectively. The intra-day and inter-day relative standard deviations for the measurements of quality control samples were less than 3.1%. The stability of the plasma samples was also validated. This method was successfully used to study the pharmacokinetics and bioavailability of CA in rats.

Gel permeation chromatography: A guide to data interpretation

Abstract: The importance of data produced by gel permeation chromatography (GPC) in polymer engineering is emphasized. The steps involved in deriving a differential molecular weight distribution curve (dMWD) from the chromatogram obtained by GPC are outlined. Each step is described, indicating its importance. Several viscosity-molecular weight relationships, useful in the production of relevant calibration curves from the polystyrene calibration, are included. The presentation of results and experimental procedures are given.

The application of robust calibration to radioimmunoassay.

Abstract: The minute concentrations of many biochemically and clinically important substances are currently estimated by radioimmunoassay (RIA). Traditionally, the most popular approaches to the statistical analysis of RIA data have been to linearize the data through transformation and fit the calibration curve using least squares or to directly fit a nonlinear calibration curve using least squares. Estimates of the hormone concentration in patients are then obtained using this curve. Unfortunately, the transformation is frequently unsuccessful in linearizing the data. Furthermore, the least squares fit can lead to erroneous results in both approaches since the many sources of error which exist in the RIA process often result in outlier observations. In this paper, an approach to the analysis of RIA data which incorporates robust estimation methods is described. An algorithm is presented for obtaining the M-estimates of nonlinear calibration curves. The curves to be fitted are modified hyperbolae based on 12 to 16 observations. A procedure, based on the application of the Bonferroni Inequality, is presented for obtaining tolerance-like interval estimates of the concentration of the hormone of interest in the patients. Results of simulations are cited to support the method of construction of confidence bands for the fitted calibration curve. Data obtained from the Veteran's Hospital, Buffalo, New York are used to illustrate the application of the algorithm which is presented.

Comparison of single- and multivariate calibration for determination of Si, Mn, Cr and Ni in high-alloyed stainless steels by laser-induced breakdown spectrometry

Abstract: The quantitative analysis of high-alloyed steels by LIBS is usually complicated by overlap of the analytical lines with iron lines due to the complex structure of the emission spectra of each component. To overcome this problem, we compared two calibration strategies in the current research work. Univariate regression analysis was used for a number of analytical lines of Si, Mn, Ni, and Cr with and without strong spectral interference with other lines. Several methods of data pre-processing (for example, by normalization using an internal standard or baseline correction) to compensate for matrix effects or the pulse to pulse deviations of the analytical signal have been compared with the calibration curves constructed with the use of peak intensities. As an alternative to the univariate strategy, multivariate calibration based on principal component regression (PCR) was used in this work. We examined two criteria separately to select the most predictive model. The minimal values of the relative Root Mean Square Error of Cross Validation (RMSECV, %) provided the best prediction accuracy while the use of the well known F-criterion reduced the number of principal components up to 4 or 5 for each analyte without significant worsening of prediction capability. The measurements within four spectral windows (210–230 nm, 280–300 nm, 345–365 nm and 400–420 nm) were carried out on a set of 10 standard samples. Univariate calibration for Cr, Ni and Mn provided the best prediction (R2 = 0.996) if an appropriate reference line could be found and analytical lines were not overlapped with others. The best prediction for Si (R2 = 0.94) was obtained with the use of a peak signal of the Si 212.41 nm line without normalization. Otherwise, PCR provided good predictive capability (RMSECV, % = 3, 4, 5 and 9 of quantification of Mn, Cr, Ni and Si, respectively) in the spectral ranges where numerous matrix lines strongly interfered with analytical lines.