EVALUATION OF NONLINEARITY TESTING PROCEDURES ON SIMULATED DATA

When calibrating a method, use of a straight line is highly favorable because it is easy to compute sensitivity and the blank to be used to predict an unknown concentration, Therefore, when validating an analyt ical method, it is necessary to check whether linearity is acceptable over the method's whole application range before trying another model, Available procedures for checking linearity are reviewed by using a s imulation model that gives a complete family of curved calibration lin es, From the simulated data, it is possible to compute the prediction error generated by the model curvature as the relative difference betw een the linear extrapolated value and the observed value, It appears t hat the power of the classical ''linearity test'' depends on experimen tal design and that at least 25 measurements are necessary to detect c urvature for an acceptable prediction error, An alternative model-fitt ing criterion, based on the chi(2) probability law, also was evaluated , It is also applicable but seems less stable and more sensitive to da ta size, The question of the definition of nonlinearity is also raised because it is directly connected to the comparison of nonlinearity de tection techniques.