| 摘 要： | The calibration curve is based on a deterministic relationship between Y,the observed response,f,theinstrumental function,and x,the concentration of the analyte.When the instrumental function is linearthe model isY=a_0+a_1x+εwith a_0 the blank and a_1 the sensitivity.In fact calibratio is a two-step procedure consisting of the‘calibration’itself(computation of the curvecoefficients)and the‘counter-calibration’,when the curve is used to predict the concentration of anunknown sample.Least-squares regression(LSR)can be simply used in order to solve the first step.However,an error may occur when the classical results of LSR are used to compute the detection limit.The detection limit can be defined as the smallest concentration that can be detected with certainty fromthe blank with a risk of error α and a probability of being right 1-β.If we define the critical level(CL)asy(CL)=a_0+t_(1-α)(var(a_0))~(1/2)the detection limit(DL)is theny(DL)=y(CL)+t_(1-β)(var(a_0))~(1/2)Since the units used to express y(DL)are the same as the units of y,it must be divided by α_1 in order to beexpressed as a concentration.This creates a contradiction however,because α_1 is a random variable.Therefore the detection limit cannot be computed according to its previous definition.If we use tolerance interval theory,it is possible to define a new criterion called the minimumallowable concentration which does not have such a drawback.This new concept is based on an improvedapproach to counter-calibration.
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