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Analytical Chem istry - DePauw University

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54 <strong>Analytical</strong> <strong>Chem</strong><strong>istry</strong> 2.0( S −S )= S = kntotal reag A A( S −S )= S = kCtotal reag A AMethods for effecting this separation arediscussed in Chapter 7.By itself, a method blank cannot compensate for an interferent that ispart of the sample’s matrix. If we happen to know the interferent’s identityand concentration, then we can be add it to the method blank; however,this is not a common circumstance. A more common approach is to finda method for separating the analyte and interferent by removing one fromthe sample. Once the separation is complete, we can proceed with theanalysis using equation 3.9 or equation 3.10.3E.2 CalibrationA simple definition of a quantitative analytical method is that it is a mechanismfor converting a measurement, the signal, into the amount of analytein a sample. Assuming that we can correct for the method blank and thatwe can compensate for interferents, a quantitative analysis is nothing morethan solving equation 3.1 or equation 3.2 for n A or C A .To solve these equations we need the value of k A . For a total analysismethod we usually know the value of k A because it is defined by the stoichiometryof the chemical reactions generating the signal. For a concentrationmethod, however, the value of k A usually is a complex function of experimentalconditions. A Calibration is the process of experimentally determiningthe value of k A by measuring the signal for one or more standardsamples, each containing a known concentration of analyte. With a singlestandard we can calculate the value of k A using equation 3.1 or equation3.2. When using several standards with different concentrations of analyte,the result is best viewed visually by plotting S A versus the concentration ofanalyte in the standards. Such a plot is known as a calibration curve, anexample of which is shown in Figure 3.6.Figure 3.6 Example of a calibrationcurve. The filled circles (• )are the individual results for thestandard samples and the line isthe best fit to the data determinedby a linear regression analysis. SeeChapter 5 for a further discussionof calibration curves and an explanationof linear regression.S A10.80.60.40.200.00 0.20 0.40 0.60 0.80 1.00Concentration of Analyte (µg/mL)

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