We report a comprehensive
study of the efficacy of least-squares
fitting of multidimensional spectra to generalized Kubo line-shape
models and introduce a novel least-squares fitting metric, termed
the scale invariant gradient norm (SIGN), that enables a highly reliable
and versatile algorithm. The precision of dephasing parameters is
between 8× and 50× better for nonlinear model fitting compared
to that for the centerline-slope (CLS) method, which effectively increases
data acquisition efficiency by 1–2 orders of magnitude. Whereas
the CLS method requires sequential fitting of both the nonlinear and
linear spectra, our model fitting algorithm only requires nonlinear
spectra but accurately predicts the linear spectrum. We show an experimental
example in which the CLS time constants differ by 60% for independent
measurements of the same system, while the Kubo time constants differ
by only 10% for model fitting. This suggests that model fitting is
a far more robust method of measuring spectral diffusion than the
CLS method, which is more susceptible to structured residual signals
that are not removable by pure solvent subtraction. Statistical analysis
of the CLS method reveals a fundamental oversight in accounting for
the propagation of uncertainty by Kubo time constants in the process
of fitting to the linear absorption spectrum. A standalone desktop
app and source code for the least-squares fitting algorithm are freely
available, with example line-shape models and data. We have written
the MATLAB source code in a generic framework where users may supply
custom line-shape models. Using this application, a standard desktop
fits a 12-parameter generalized Kubo model to a 106 data-point
spectrum in a few minutes.