Summarized Procedure to Determine Best-Fit Calibration Curve

7 Summarized Procedure to Determine Best-Fit Calibration Curve 

The several possibihties described in Section 8.3 may seem confnsing bnt in practice the problem is not usually too comphcated for analytical methods based on mass spectrometric detection, as a resnlt of the excellent degree of linearity observed for mass spectrometric response over ranges of several orders of magnitnde in the concentration (amonnt) of anal5d e introdnced. The best way to proceed will vary with the details of the reqnired analysis and the 

By linear least-squares regression we mean fitting experimental data (Xj,Yi) to a theoretically justified function Yipred = f(Xj, A, that is such that the partial derivatives (e.g., d S,jl)/8A) with respect to each of the 

An example of a more complex function that is linear in this sense is Yi pred = A + 5.exp(x ), but Yj p d = A + 5.exp(C.X ) can not be fitted by linear least-squares since d(S, j )/dC is not linear in C in such cases there is no simple algebraic solution for the parameters as there is for the linear case (e.g., Equations [8.21]-[8.24]), so we must use nonlinear regression techniques that involve successive iterations to the final best result. 

If iteration starts here result is a false minimum 

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