While single buffered systems can be described using the Henderson-Hasselbalch equation, developing models for multiple poly-protic buffers (e.g., phosphate and citrate) remains an ongoing challenge. Commercial and literature-reported pH adjustment strategies are typically either based on proportional-integral-derivate (PID) control or model predictive control (MPC); both come with limitations. The PID control strategy continuously calculates deviation of the measured value from the target value, and applies a correction based on a proportional, integral or derivative correction strategy. A typical example are bioreactors which often operate in fed-batch mode, relying on PID pH control to allow for the maintenance of specific conditions required by the biological cultures. While no chemical information except the continuous measured pH is needed for PID, a loss of information needs to be accepted since insights into the chemical system cannot be implemented into future pH adjustments, as opposed to model based strategies. More recent pH adjustment approaches are based on MPC arrays, e.g., Altinten et al. describe a generalized predictive control for continuous flow pH adjustment , Helmy et al. relied on multi-linear regression and Alkamil et al. used a fuzzy artificial neural network (ANN) outperforming a PID-control. Others have also expanded on using ML based MPC strategies for this same purpose. A major challenge associated to MPC-based pH adjustment is operating in a low data regime, particularly of interest for highthroughput small-scale pH adjustment, as opposed to continuous pH adjustment. Aside from automated strategies, pH adjustment process is also often conducted manually in a R&D stage which is time consuming, requiring approximately five to seven minutes per sample (Table : entries 7, 10, 13).