In the agricultural and food industry, determining the chemical composition of raw materials is important for production efficiency, application, and price. Traditional laboratory testing is time-consuming, complicated, and expensive. 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.
Researchers develop near-infrared spectroscopy models to analyze corn kernels and biomass
