Abstract:
:Many variables and their interactions can affect a biotechnological process. Testing a large number of variables and all their possible interactions is a cumbersome task and its cost can be prohibitive. Several screening strategies, with a relatively low number of experiments, can be used to find which variables have the largest impact on the process and estimate the magnitude of their effect. One approach for process screening is the use of experimental designs, among which fractional factorial and Plackett-Burman designs are frequent choices. Other screening strategies involve the use of artificial neural networks (ANNs). The advantage of ANNs is that they have fewer assumptions than experimental designs, but they render black-box models (i.e., little information can be extracted about the process mechanics). In this paper, we simulate a biotechnological process (fed-batch growth of baker's yeast) to analyze and compare the effect of random experimental errors of different magnitudes and statistical distributions on experimental designs and ANNs. Except for the situation in which the error has a normal distribution and the standard deviation is constant, it was not possible to determine a clear-cut rule for favoring one screening strategy over the other. Instead, we found that the data can be better analyzed using both strategies simultaneously.
journal_name
J Ind Microbiol Biotechnoljournal_title
Journal of industrial microbiology & biotechnologyauthors
Soria MA,Gonzalez Funes JL,Garcia AFdoi
10.1007/s10295-004-0171-4subject
Has Abstractpub_date
2004-11-01 00:00:00pages
469-74issue
10eissn
1367-5435issn
1476-5535journal_volume
31pub_type
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