AI-Powered Corn Research Improves Chip Production Quality

By Scout Nelson

A perfect corn chip depends on achieving the right moisture level in masa dough, which affects texture, taste, nutrition, and processing efficiency. Masa is produced through nixtamalization, a process that involves soaking and cooking corn before it is turned into dough.

Researchers led by Professor Candice Hirsch in the Department of Agronomy and Plant Genetics have developed a machine-learning model to help predict masa moisture content from raw grain samples. The project is part of a long-term collaboration with PepsiCo focused on improving corn traits and food manufacturing technologies.

The model, called CHIP-NCM, analyzes scanned corn grain before processing begins. This allows manufacturers to better estimate cooking times and temperatures, helping improve efficiency and reduce production waste.

The technology enables processors to make more informed decisions before production rather than relying solely on test batches. As a result, manufacturers may improve consistency while reducing unnecessary resource use.

The research extends beyond food production. Corn is used in a wide range of products, including foods, clothing materials, personal care items, footwear, diapers, and mattresses, making crop improvement important across multiple industries.

A key focus of the team’s work is improving corn genetics to support sustainability and climate resilience. Researchers are studying genetic variation to develop corn varieties that can better withstand environmental challenges while meeting industry-specific quality requirements.

Students play an important role in the research program through hands-on laboratory and field experiences. These opportunities help them build skills in genetics, data analysis, crop science, and experimental design while preparing for careers in agriculture and private industry.

By combining machine learning, crop genetics, and practical education, the research is advancing corn production, supporting innovation in food manufacturing, and helping train the next generation of agricultural scientists.

Photo Credit: gettyimages-vladans