That perfect, crunchy bite of a corn chip starts with knowing exactly how much moisture in the corn. Masa dough, used to make products like corn chips, starts with soaking and cooking the corn grain. This process is called nixtamalization. The moisture content of the dough affects the taste, nutrition, and if it will stick to the machines or not as it goes through the chip making process. Each corn product requires a different moisture content in the dough.
Nailing down that exact moisture content number is where Professor Candice Hirsch and her team in the Department of Agronomy and Plant Genetics comes in. Over the last decade, they have collaborated with the multinational company PepsiCo on a number of projects to improve corn traits and technologies for the food industry. They created a machine-learning model that can fairly accurately predict the moisture content in masa dough made from the raw grain scanned when it arrives at a grain elevator. The model is called CHIP-NCM, standing for corn hybrid and inbred prediction of nixtamalization moisture content.
“From a small sample of raw grain, you can see the need to cook longer, cook shorter or change the temperature,” Hirsch said. “In theory, this will reduce food waste, because with every batch they have to run a test batch through first to see how well it’s doing, leading to food waste. Now they can adjust [their cooking methods] preemptively.”
Source : umn.edu