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 (QY2) 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.