In Illinois, new research from the University of Illinois Urbana-Champaign demonstrates that near-infrared (NIR) spectroscopy and machine learning can provide quick, accurate, and cost-effective product analysis. In two studies, the researchers explore the use of NIR spectroscopy for analyzing characteristics of corn kernels and sorghum biomass.
In the first study, the researchers created a global model for corn kernel analysis. Moisture and protein content impact nutritional value, processing efficiency, and price of corn, so the information is crucial for the grain processing industry. The research is published in the journal Food Chemistry.
NIR and other spectroscopic techniques are indirect methods. They measure how a material absorbs or emits light at different wavelengths, then construct a unique spectrum that is translated into product characteristics with machine learning models. Many food and agricultural processing facilities already have NIR equipment, but models need to be trained for specific purposes.
In the second study, the researchers focused on sorghum biomass, which can serve as a renewable, cost-effective, and high-yield feedstock for biofuel. The research is published in the journal Biomass and Bioenergy.
Using sorghum from the University of Illinois Energy Farm, they were able to accurately and reliably predict moisture, ash, lignin, and other features.
Tags: Illinois, University of Illinois Urbana-Champaign
Category: Research
