Calibration Methods.ppt
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1、Calibration Methods,Introduction1.) Graphs are critical to understanding quantitative relationships One parameter or observable varies in a predictable manner in relationship to changes in a second parameter 2.) Calibration curve: graph showing the analytical response as a function of the known quan
2、tity of analyte Necessary to interpret response for unknown quantities,Time-dependent measurements of drugs and metabolites in urine samples,Generally desirable to graph data to generate a straight line,Calibration Methods,Finding the “Best” Straight Line1.) Many analytical methods generate calibrat
3、ion curves that are linear or near linear in nature(i) Equation of Line:where: x = independent variabley = dependent variablem = slopeb = y-intercept,Calibration Methods,Finding the “Best” Straight Line2.) Determining the Best fit to the Experimental Data (i) Method of Linear Least Squares is used t
4、o determine the best values for “m” (slope) and “b” (y-intercept) given a set of x and y values Minimize vertical deviation between points and lineUse square of the deviations deviation irrespective of sign,Calibration Methods,Finding the “Best” Straight Line4.) Goodness of the Fit (i) R2: compares
5、the sums of the variations for the y-values to the best-fit line relative to the variations to a horizontal line. R2 x 100: percent of the variation of the y-variable that is explained by the variation of the x-variable. A perfect fit has an R2 = 1; no relationship for R2 0,R2=0.9952,99.5% of the y-
6、variation is due to the x-variation,R2=0.5298,53.0% of the y-variation is due to the x-variation What is the other 47% caused by?,Very weak to no relationship,Strong direct relationship,R2 based on these relative differences Summed for each point,Calibration Methods,Calibration Curve1.) Calibration
7、curve: shows a response of an analytical method to known quantities of analyte,Procedure: Prepare known samples of analyte covering convenient range of concentrations.Measure the response of the analytical procedure.Subtract average response of blank (no analyte).Make graph of corrected response ver
8、sus concentration.Determine best straight line.,Calibration Methods,Calibration Curve2.) Using a Calibration CurvePrefer calibration with a linear response- analytical signal proportional to the quantity of analyteLinear range- analyte concentration range over which the response is proportional to c
9、oncentrationDynamic range- concentration range over which thereis a measurable response to analyte,Additional analyte does not result in an increase in response,Calibration Methods,Calibration Curve3.) Impact of “Bad” Data Points Identification of erroneous data point.- compare points to the best-fi
10、t line- compare value to duplicate measures Omit “bad” points if much larger than average ranges and not reproducible.- “bad” data points can skew the best-fit line and distort the accurate interpretation of data.,Remove “bad” pointImprove fit and accuracy of m and b,y=0.16x + 0.12 R2=0.53261,y=0.09
11、1x + 0.11 R2=0.99518,Calibration Methods,Calibration Curve4.) Determining Unknown Values from Calibration Curves(i) Knowing the values of “m” and “b” allow the value of x to be determined once the experimentally y value is known. (ii) Know the standard deviation of m & b, the uncertainty of the dete
12、rmined x-value can also be calculated,Calibration Methods,Calibration Curve4.) Determining Unknown Values from Calibration Curves (iii) Example:,The amount of protein in a sample is measured by the samples absorbance of light at a given wavelength. Using standards, a best fit line of absorbance vs.
13、mg protein gave the following parameters:m = 0.01630 sm = 0.00022 b = 0.1040 sb = 0.0026An unknown sample has an absorbance of 0.246 0.0059. What is the amount of protein in the sample?,Calibration Methods,Calibration Curve5.) Limitations in a Calibration Curve (iv) Limited application of calibratio
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