<p>A team of University of Toronto Engineering researchers, led by Professor <a href="https://discover.research.utoronto.ca/13303-timothy-chan/"><strong>Timothy Chan</strong></a>, is leveraging machine learning to optimize the macronutrient content of pooled human donor milk recipes. The researchers introduce their data-driven optimization model in a <a href="https://pubsonline.informs.org/doi/full/10.1287/msom.2022.0455">new paper published</a> in <em>Manufacturing and Systems Operations Management</em>. </p>
<p>In the first phase, researchers collected the necessary data to create a machine learning model to predict the macronutrient content of the pooled recipes, and then designed an optimization model to create the recipes based on macronutrient requirements, that is, the necessary levels of protein and fat. The team then created a simulation model to test the method before embarking on an experiment in the milk bank, which took place over 16 months in 2021 and 2022. The last phase of the study began by observing the milk bank’s operation for six months and measuring the fat, protein and bacteria levels in the pooled recipes. For the following six months, the milk bank used the data-driven optimization framework to create the pooled milk recipes. At the end of the year, the researchers compared the optimized recipes to the previous ones to assess which recipes met the macronutrient targets. </p>
<p>"We found that our pooled recipes met the bar for protein and fat simultaneously up to 75% more often, without compromising other factors like an increase in bacteria,” says Chan. “And it took us 60% less time to make the recipes.”</p>
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