) is similar to the sE value (9.02 x 10 -4 ) for the corresponding re-parameterized fit. These results show that the reparameterized model exhibits better convergence properties as compared to the original model when the data have a high signal-to-noise ratio. K and Q). Clearly, the parameter predictions for the original parameterization are much worse. This can also be seen in the distributions of the parameter errors shown in the third column of Fig. . The average errors for the two parameterizations are summarized in Table As a further test of the ability of the re-parameterized NK model to yield reliable results, we predicted isocratic retention factors using the NK parameters extracted from the gradient fits. Somewhat arbitrarily, we evaluated the retention factors at the experimentally obtained organic compositions for the isocratic data that were within the valid range of the fits, as described by Eq. ( ) (about 1200 comparisons in each case, see row 14 in Table ), and accuracy was assessed relative to the isocratic retention factors calculated using the reference NK parameters. The distribution of the errors for the prediction of these isocratic k values is shown in Fig. for all three noise levels and both the original and the ref = 0.3 parameterizations. It can be seen that the accuracy of the predictions is improved when using the re-parameterized model (average errors shown in row 15 of Table ). For the low noise level, the average percent relative error in retention factor was 0.0494 % and the maximum error was 0.369% (see rows 15 and 16, Trial 2, Table ). Even at the high noise level, the average and maximum errors in isocratic retention factors were 0.504 % and 6.31 %, which is still quite good (see rows 15 and 16, Trial 5, Table ), and much better than reported previously by Brau et al. .