  High throughput experimental systems play an important role in bioprocess
development, as they provide an efficient way of analysing different
experimental conditions and perform strain discrimination in previous phases to
the industrial scale production. In the millilitre scale, these systems are
combinations of parallel mini-bioreactors, liquid handling robots and automated
workflows for data handling and model based operation. For successfully
monitoring cultivation conditions and improving the overall process quality by
model-based approaches, a proper model identification is crucial. However, the
quality and amount of measurements makes this task challenging considering the
complexity of the bio-processes. TheDissolved Oxygen Tension is often the only
measurement which is available online, and therefore, a good understanding of
the errors in this signal is important for performing a robust estimation.Some
of the expected errors will provoke uncertainties in the time-domain of the
measurement, and in those cases, the common Weighted Least Squares estimation
procedure can fail providing good results. Moreover, these errors will have
even a larger effect in the fed-batch phase where bolus feeding is applied, as
this generates fast dynamic responses in the signal. In the present work, an
insilico study of the performance of Weighted Least Squares estimator is
analysed when the expected time-uncertainties are present in the oxygen signal.
As an alternative, a loss based on the Dynamic Time Warping measure is
proposed. The results show how this latter procedure outperforms the former
reconstructing the oxygen signal, and in addition, returns less biased
parameter estimates.
