Mathematical modeling for food product development or unit operations modification to produce a food is increasing and adopting some statistical techniques, such as response surface methodology (RSM), to solve problems where several independent variables (or factors) influence the response variable value. Technical problems, data analysis and modeling, experimental design and independent variables’ choice make food scientist and developers harder engagement. Variable selection and optimization of outcome of desired product is challenging task. For this purpose, scientist should aim at determining optimum levels of the main ingredients/compounds in order to obtain suitable responses from desired properties (like aromas, color, physicochemical, rheological and sensory parameters). Therefore, by using statistical techniques, ingredients and their variation range can be tested with a minimum number of experiments while reducing energy, time and cost of testing. This procedure is crucial when laboratory testing of power ultrasound processing of food product should be scaled-up. Scientist that deals with this area of novel non-thermal food processing knows the problem of scaling up, and the procedure is mostly not linear when comparing results from small volumes to large ones. Therefore, experimental planning and design would lead to energy, time and consumables saving, and to be environmentally friendly.

Food processing technology