Improving the productivity of fed-batch filamentous fungal fermentations can be formulated as a dynamic optimization problem. However, numerical optimization based on a nominal process model is typically insufficient when uncertainty in the form of model mismatch and process disturbances is present. This paper considers a measurement-based approach that consists of tracking the necessary conditions of optimality (NCO tracking). For this, the nominal input profiles are dissected into time functions and constant parameters that are assigned to the various parts of the NCO. These input elements are then adapted using appropriate measurements. NCO tracking is used here to maximize enzyme production in the case of parametric uncertainty. Upon parameterization of the feed rate input, only point constraints need to be met for optimality, which can be achieved on a run-to-run basis. The approach is illustrated in simulation and compared to open-loop application of the nominal optimal solution.