The modern digitalization of research facilities allows the relatively fast and easy accumulation of experimental data, which can be used to accelerate subsequent workflows by employing transfer learning (TL). TL represents the method of pretraining ML models for one task and subsequentially using the trained model for a similar prediction task. Typically, the auxiliary model would profit from an availability of large pretraining datasets, whereas the target model is fine-tuned on a smaller dataset. Process chemists often modify the composition of formulated products (e.g., liquid laundry detergents) to fine tune product properties (e.g., viscosity). While small modifications do not change the overall composition greatly, the pH response (titration curve) does change and thus the sample requires a new titration strategy every time.