Industrial fermentation end-product modelling with multilinear PLS
In this paper, a trilinear version of the partial least squares (PLS) algorithm was used to model the performance of an industrial fed-batch fermentation process. Trilinear data obtained from process operation were used to derive a model for the end-process active product ingredient (API) concentration prediction. Obtained mutilinear PLS models were compared with the correspondent bilinear models. A genetic algorithm was used to select appropriate calibration sets (to reduce the influence of nominal batches). A validation coefficient of determination (Q(Y)(2)) of 91.4% was obtained for the multilinear PLS model after batch selection (prediction intervals were estimated using bootstrapping). Examination of the multilinear PLS model weights led to the delimitation of a small time region (from 50 to 75 processing hours) almost exclusively responsible for the fermentation performance. (C) 2003 Elsevier B.V. All rights reserved.
multilinear pls genetic algorithms pharmaceutical production multivariate modelling parafac