Application of artificial intelligence and machine learning to food rheology
Book Chapter
Ahmad, I, Benjamin, TA. (2022). Application of artificial intelligence and machine learning to food rheology
. 201-219. 10.1016/B978-0-12-823983-4.00004-2
Ahmad, I, Benjamin, TA. (2022). Application of artificial intelligence and machine learning to food rheology
. 201-219. 10.1016/B978-0-12-823983-4.00004-2
This chapter reviews alternative modeling approaches to conventional modeling using evolutionary algorithms and machine learning (ML) principles. With the increasing computational power, artificial intelligence (AI) is being used to solve a multitude of engineering problems. AI is a collective term used to describe ML and Deep Learning (DL) techniques. The difference between ML and DL needs to be understood as both terms are used interchangeably and are sometimes confusing. Both approaches refer to AI and a subset of AI. While both approaches are the hallmark of the new shift in scientific discovery using data-driven approaches. Modeling of the flow behavior of liquids, semisolid and textural behavior under stress have been covered. ML models are able to efficiently characterize these kinds of rheological properties, along with withstanding temperature changes and flow instability. With ML and a DL approach, rheological models are able to tackle foods that exhibit complex visco-elastic properties.
