Over 3200 discrete soybean samples were obtained from production locations around the United States during the years 2012-2016. Ground samples were scanned on near infrared spectrometers (NIRS) and analyzed by HPLC for total isoflavone and total saponin composition, as well as total carbohydrate composition. Multiple Linear Regression (MLR) analysis of preprocessed spectral data was used to develop optimized models to predict isoflavone content. The selection of a suitable calibration model was based on a high regression coefficient (R2), and lower standard error of calibration (SEC) values. Robust validated predictions were obtained for isoflavones, however less than robust calibrations were obtained for the total saponins. The correlations were not as robust for predicting the carbohydrate composition. NIRS is a suitable, rapid, nondestructive method to determine isoflavone composition in ground soybeans. Useful isoflavone composition predictions for large numbers of soybean samples can be obtained from quickly obtained NIRS scans.
Keywords: Analysis; Carbohydrates; Composition; Isoflavones; Near infrared spectrometry; Saponins; Soybean.
Published by Elsevier Ltd.