Classification of fermentation performance by multivariate analysis based on mean hypothesis testing.

Multivariate analysis, such as principal component analysis and artificial autoassociative neural networks, is currently extensively applied to feature capturing, physiological state recognition, fault detection and bioprocess control. However, it is not clear which process variable should be selected as an important input for multivariate analysis to analyze physiological conditions and/or bioprocess performance a priori. An efficacious method to select more informative process variables from the repository of historical data is highly desired. In this study, we focused on a premodeling step. Mean hypothesis testing (MHT) was used to select appropriate variables for multivariate analysis. Fermentation data sets were classified into two classes "good" and "bad" according to the MHT results. The results showed that selecting discriminating process variables from the historical database by MHT enhanced the overall effectiveness of multivariate analysis prior to principal component analysis and artificial autoassociative neural network model creation.

[1]  S Bicciato,et al.  Mining of biological data I: identifying discriminating features via mean hypothesis testing. , 2000, Metabolic engineering.

[2]  M. Kramer.  Nonlinear principal component analysis using autoassociative neural networks , 1991 .

[3]  M. Goldstein,et al.  Multivariate Analysis: Methods and Applications , 1984 .

[4]  S Bicciato,et al.  Mining of biological data II: assessing data structure and class homogeneity by cluster analysis. , 2000, Metabolic engineering.

[5]  G Stephanopoulos,et al.  Fermentation database mining by pattern recognition. , 1997, Biotechnology and bioengineering.

[6]  David Leake,et al.  Case-Based Reasoning: Experiences, Lessons and Future Directions , 1996 .

[7]  Ward,et al.  Fermentation seed quality analysis with self-organising neural networks , 1999, Biotechnology and bioengineering.

[8]  Toshiomi Yoshida,et al.  Real-time qualitative analysis of the temporal shapes of (bio) process variables , 1992 .

[9]  Douglas B. Kell,et al.  GMP — good modelling practice: an essential component of good manufacturing practice , 1995 .

[10]  Hiroshi Shimizu,et al.  On-line fault diagnosis for optimal rice α-amylase production process of a temperature-sensitive mutant of Saccharomyces cerevisiae by an autoassociative neural network , 1997 .

[11]  H Shimizu,et al.  Bioprocess Fault Detection by Nonlinear Multivariate Analysis: Application of an Artificial Autoassociative Neural Network and Wavelet Filter Bank , 1998, Biotechnology progress.

[12]  B Lennox,et al.  Process monitoring of an industrial fed-batch fermentation. , 2001, Biotechnology and bioengineering.