Two methods for generating smoothing splines are compared and applied
to data from a fed-batch fermentation process. One method chose both t
he degree of the spline and its parameters by minimizing the generaliz
ed cross validation (GCV) function using a genetic algorithm (GA). The
other method adjusted the smoothing spline to a specified chi-square
goodness-of-fit, requiring prior knowledge of the measurement variabil
ity. The GCV/GA method led to excellent results with all the fermentat
ion data records. The goodness-of-fit method gave a family of spline f
its; splines with a low percentage fit extracted trends from the data,
while for general use a 50% fit appeared satisfactory. The goodness-o
f-fit method executed more quickly than the GCV/GA method, but the GCV
/GA method was more generally applicable as it chose both the degree o
f the spline and the amount of smoothing automatically.