A robust batch-to-batch optimization (RB2BO) is proposed for recipe optimization of batch processes in the presence of uncertainty. Robust optimization can result in performance loss due to its conservative nature, and measurement-based optimization approaches do not typically address the residual uncertainty after parameter identification. In the proposed approach, distribution and extreme-case scenarios of uncertain parameters are refined through Bayesian updating using the measurements, and then the robust optimization is performed with the updated scenarios. Due to the iterative scenario adaptation and robust optimization mechanism, the proposed RB2BO approach is intended to provide robust recipes less conservative compared to the conventional methods, resulting in high objective function values and minimal constraint violations. This is demonstrated through an example of a pectin extraction process by considering feedstock variabilities and terminal constraints for the desired product quality. In addition, the termination criteria of the batch-to-batch correction are suggested, and the strategies for the parameter estimation and measurement are discussed to improve the performance of the adaptation.