Knowledge Bank

Soybeans are one of the world’s most valuable crops due to their high protein and fat content. Traditional methods to determine the nutritional profile are time consuming and require benchtop equipment. Non-destructive and rapid techniques, such as vibrational spectroscopy in the Near Infrared region (780-2526 nm), can be paired with chemometrics to rapidly assess soybean quality traits. This project explored the capability of handheld NIR scanners to rapidly predict protein, moisture, fat content, and fatty acid profile of soybeans for accurate pricing of crops and monitor traits of gene edited soybeans. The partial least squares regression model predicted the protein, oleic, linoleic, and linolenic acid with a correlation coefficient of 0.90 or greater in 90 samples. The handheld NIR scanner performed similarly to the benchtop NIR equipment. The model differentiated between the high oleic variety, which is more stable against lipid oxidation, and conventional soybeans with distinguishing bands at 1702 and 1683 cm-1. Soxhlet and Karl Fischer methods overestimated the fat and moisture values, respectively. NIR scanners show great promise in implementation of real-time quality testing of soybeans with limited sample preparation and training. This technology will allow farmers to price soybean crops based on higher-quality characteristics and help predict quality traits of genetically modified strains of soybeans.