期刊
INNOVATIVE FOOD SCIENCE & EMERGING TECHNOLOGIES
卷 83, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ifset.2022.103232
关键词
Automated robotic platform; Machine learning; Multiobjective optimization; Whey protein isolate
This study designed a fully automized milli-fluidic laboratory and successfully optimized the aggregation process of a liquid formulation using the TSEMO algorithm. Within 48 hours, 90 experiments were completed without human intervention, resulting in a Pareto front of 18 optimal recipes. It demonstrates an actively learning, self-driving food formulation process.
The creation and optimization of formulated products represents a major challenge for science and industry in the food sector. Thereby, different raw materials are mixed and processed to meet predefined and often competing targets. During this procedure, applied experimental campaigns not only require expert knowledge, but, depending on the complexity, also cause a high consumption of resources and costs. In the present work, a fully automized milli-fluidic laboratory driven by the Thomsen sampling efficient multiobjective optimization (TSEMO) algorithm was designed. The methodology was successfully applied to optimize the aggregation process of a liquid formulation consisting of whey protein isolate, NaCl and CaCl2. Within 48 h 90 experiments could be performed without human intervention, resulting in a Pareto front formed by a set of 18 optimal recipes. It is thus a successful demonstration of an actively learning, self-driving food formulation process.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据