Sequential Design of Experiments (DoE) Campaigns for High Hydrostatic Pressure (HHP) Process Optimization

Data are obtained within the framework of RSU grant "AI&HHP4Bi: Artificial intelligence and high hydrostatic pressure for sterile biomaterials" (6-ZD-22/8/2023). The overall goal is to evaluate technological process, method limitations of E.coli bacteria during sterilisation by high hydrostatic pressure. This dataset contains results from sequential Design of Experiments (DoE) campaigns aimed at optimizing a High Hydrostatic Pressure (HHP) process to minimize microbial colonies (colonies_ec) and total effort. The experiments were guided by Genetic Algorithms, iterating through various combinations of pressure, cycles, and time. This dataset supports research into efficient HHP process parameters for microbial reduction while considering process effort. The software used in the project: xT SAAM https://www.x-t.ai/xt-saam/