This research revolves around utilising Named Entity Recognition (NER) to analyse and categorise data from English translations of pharmacopeias from the Abbasid era, noted for its valuable contributions to science and medicine. The main goal of this work, along with publishing this resource freely, is to assess crossmanuscript NER performance by evaluating the NER model's performance on unseen corpora and translation styles, as well as demonstrating the transferability of the NER task on such corpora. Two distinct experiments were conducted, focusing on F1-scores differences from mixing source translators and varying training dataset sizes. In experiments involving mixing translator styles, training on a mix of all available styles while accounting for dataset size yielded the best F1-scores compared to even training on the same style as the testing data, while experiments with dataset sizes show diminishing returns of scaling training datasets compared to varying translation styles. This work attempts to enhance the exploration of the medical knowledge embodied in these texts to facilitate their analysis for knowledge extraction relevant to modern medical practices. Furthermore, this research demonstrates strategies to optimise NER results in this context, forming a juncture between digitising historical information and enabling further explorations in pharmacopeia-related Natural Language Processing research.