Researchers in the University of Minnesota's Department of Agronomy and Plant Genetics are using machine learning to make corn-based foods such as corn chips more consistent, Farms.com reported. The team, led by Professor Candice Hirsch, works with PepsiCo to understand how the traits of the grain affect processing.
The focus is masa dough, made by nixtamalization, whose moisture content shapes the texture, flavour and nutrition of the final product and the efficiency of the factory. The team built CHIP-NCM, a machine-learning model that predicts masa moisture from scans of raw corn grain, so manufacturers can adjust processing before production starts, improving efficiency and cutting waste.
The project is part of a long-term partnership between the university and PepsiCo on corn traits and food-industry technology. The researchers are also working on corn genetics and resilience, to develop varieties that withstand environmental stress while keeping the qualities buyers need.
Corn goes into far more than food, including personal care items, clothing, footwear, diapers and household goods, which makes its performance and sustainability matter across many industries. Students take part in the laboratory and field work, building skills in genetics, crop science, data analysis and experimental design.
Photo: Ll1324 / Wikimedia Commons (CC0)
Source: Farms.com
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