In the text which follows we shall examine in numerical detail the decision levels and detection limits for the Fenval-erate calibration data set ( set-B ) provided by D. Kurtz (17). In order to calculate said detection limits it was necessary to assign and fit models both to the variance as a function of concentration and the response (i.e., calibration curve) as a function of concentration. No simple model (2, 3 parameter) was found that was consistent with the empirical calibration curve and the replication error, so several alternative simple functions were used to illustrate the approach for calibration curve detection limits. A more appropriate treatment would require a new design including real blanks and Fenvalerate standards spanning the region from zero to a few times the detection limit. Detailed calculations are given in the Appendix and summarized in Table V. [Pg.58]

Decision and Detection — Linear Calibration Curves. Before examining the actual Fenvalerate GC data, let us consider the basic linear calibration relations. (What follows was inspired in part by Hubaux and Vos (14), to which the reader might refer for supplemental detail.) If we represent a straight-line calibration as... [Pg.58]

Fenvalerate Detection Limits. To the extent that detection limits require knowledge of the calibration curve and random error (for x) as a function of concentration, all of the foregoing discussion is relevant — both for detection and estimation. However, curve shape and errors where x x, are relatively unimportant at the detection limit, in contrast to direct observations of the initial slope and the blank and its variability. (It will be seen that the initial observation in the current data set exceeded the ultimate detection limit by more than an order of magnitude )... [Pg.63]

Single-Curve Calibration Range... [Pg.124]

Differences in calibration graph results were found in amount and amount interval estimations in the use of three common data sets of the chemical pesticide fenvalerate by the individual methods of three researchers. Differences in the methods included constant variance treatments by weighting or transforming response values. Linear single and multiple curve functions and cubic spline functions were used to fit the data. Amount differences were found between three hand plotted methods and between the hand plotted and three different statistical regression line methods. Significant differences in the calculated amount interval estimates were found with the cubic spline function due to its limited scope of inference. Smaller differences were produced by the use of local versus global variance estimators and a simple Bonferroni adjustment. [Pg.183]

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