Sentence examples for using a linear calibration from inspiring English sources

Using a linear calibration function for replicated data, the calibrated mean for each participant can be calculated as: X i * = X ¯ tot + λ X ¯ j − X ¯ tot where X ¯ tot is the grand mean of all observations, X i * is the mean of the replicate measurements for each participant, and λ is the ICC reliability coefficient [ 50].

Using a linear calibration curve obtained with synthetic external standards ranging nearly two orders of magnitude, we achieved good precision (repeatability and reproducibility: 5 15%), accuracy (−3 to 15%), and ruggedness with a lower limit of quantification at 0.29 μg/ml plasma (0.15 μM).

A fully quantitative analysis using a linear calibration curve based on known standards was performed.

Cell mass was determined using a linear calibration curve relating optical density at 600 nm (OD600) and dry cell weight (R2 = 0.99).

Phytic acid concentration of the seed sample was calculated by using a linear calibration curve of y = −7.9667× + 1826.9 with correlation coefficient of 0.998 obtained against the concentration range of 10 μg ml-1 to 125 μg ml-1 of phytic acid standard (Sigma Aldrich; P0109) [see Additional file 2].

The absorbance was transformed into a cell density value (cells/mL) using a linear calibration equation previously obtained under the same conditions (R2 = 0.9981, p<0.05).

The HPLC method used a linear calibration curve (r 2  > 0.99) generated with authentic non-radioactive compounds, such as, for example, PBR28 (>99% pure; RTI International, Research Triangle Park, NC, USA) or (R -rolipram (>98%; TocR -rolipramille, MO, USA).

Using Microsoft Excel, a linear calibration line ranging from 1 to 500 ng/mL was generated for each analyte by plotting a known concentration on the x axis and the area under the chromatogram or response (Y) on the y axis.

Quantification was performed using a linear regression analysis of the calibration curve data.

The paper proposed a plane constraint calibration method for static error calibration, whilst the dynamic errors are calibrated using a linear inverse dynamic error model.

Several different concentrations of each individual D and L amino acid standard (Sigma-Aldrich) were run, and the resulting peak areas were used to construct calibration curves using a linear regression model.