Food Product Design: A Hybrid Machine Learning and Mechanistic Modeling Approach

At
present, food products are designed by trial and error and the
sensorial ratings are determined by a tasting panel. To expedite the
development of new food products, a hybrid machine learning and mechanistic
modeling approach is proposed. Sensorial ratings are predicted using
a machine learning model trained with historical data for the food
under consideration. The approach starts by identifying a set of food
ingredient candidates and the key operating conditions in food processing
based on heuristics, databases, etc. Food characteristics such as
color, crispness, and flavors are related to these ingredients and
processing conditions (which are design variables) using mechanistic
models. The desired food characteristics are optimized by varying
the design variables to obtain the highest sensorial ratings. To solve
this gray-box optimization problem, a genetic algorithm is utilized
where the design constraints (representing the desired food characteristics)
are handled as penalty functions. A chocolate chip cookie example
is provided to illustrate the applicability of the hybrid modeling
framework and solution strategy.