Researchers develop near-infrared spectroscopy models to analyze corn kernels and biomass

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. 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. Biotechnology Agriculture Phys.org – latest science and technology news stories