ISSN:
0001-1541
Keywords:
Chemistry
;
Chemical Engineering
Source:
Wiley InterScience Backfile Collection 1832-2000
Topics:
Chemistry and Pharmacology
,
Process Engineering, Biotechnology, Nutrition Technology
Notes:
Two methods for generating smoothing splines are compared and applied to data from a fed-batch fermentation process. One method chose both the degree of the spline and its parameters by minimizing the generalized 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 variability. The GCV/GA method led to excellent results with all the fermentation data records. The goodness-of-fit method gave a family of spline fits; splines with a low percentage fit extracted trends from the data, while for general use a 50% fit appeared satisfactory. The goodness-of-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 of the spline and the amount of smoothing automatically.
Additional Material:
8 Ill.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1002/aic.690400414
