data = readtable("Book1.xlsx");
Alternatively, based on my reparameterization above you can as a first pass say everything after 
looks like saturation, take the average, and declare
looks like saturation, take the average, and declaremask = data.t < 4;
F = mean(data.x(~mask))
Now you can linearize the problem and say
To visualize
z = log(F-data.x);
plot(data.t,z,'.')
hold on
As expected, the plot is pretty linear for 
, so you can do a linear fit and visualize
, so you can do a linear fit and visualizelf = polyfit(data.t(mask),z(mask),1)
plot(data.t,polyval(lf,data.t),'-')
Then convert the linear fit parameters back to 
H = -lf(1)
G = exp(lf(2))
And then you're back to the algebra to get back to 
. This procedure, which now only relies on observing the saturation behavior, results in
. This procedure, which now only relies on observing the saturation behavior, results infigure
plot(data.t,data.x,'.')
hold on
plot(data.t,F - G*exp(-H*data.t),'-')
