Techniques involving choosing process combinations for optimisation without due consideration for relevant experimental designs is scientifically unreliable and irreproducible. Mathematical modelling, of which response surface methodology (RSM) is one, provides a precise map leading to successful optimisation. This paper identified key process variables, building the model and searching the solution through multivariate regression analysis, interpretation of resulting polynomial equations and response surface/contour plots as basic steps in adapting the central composite design to achieve process optimisation. It also gave information on appropriate RSM software packages and choice of order in RSM model and data economy in reducing the factorial experiments from large number parameter combinations to a far less number without losing any information including quadratic and interaction (if present) effects. It is expected that this paper will afford many food scientists and researchers the opportunity for adapting RSM as a mathematical model for achieving bioprocess optimisation in food systems.