This paper presents a method for the identification of nonlinear partial least square (NPLS) models embedded in macroscopic material balance equations with application to bioprocess modeling. The proposed model belongs to the class of hybrid models and consists of a NPLS submodel, which mimics the cellular system, coupled to a set of material balance equations defining the reactor dynamics. The method presented is an analog to the non-iterative partial least square (NIPALS) algorithm where the PLS inner model is trained using the sensitivity method. This strategy avoids the estimation of the target fluxes from measurements of metabolite concentrations, which is rather unrealistic in the case of sparse and noisy off-line measurements. The method is evaluated with a simulation case study on the fed-batch production of a recombinant protein, and an experimental case study of Bordetella pertussis batch cultivations. The results show that the proposed method leads to more consistent models with higher statistical confidence, better calibration properties and reinforced prediction power when compared to other dynamic (N)PLS structures.