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ARS Home » Midwest Area » Peoria, Illinois » National Center for Agricultural Utilization Research » Functional Foods Research » Research » Publications at this Location » Publication #363181

Research Project: Innovative Processing Technologies for Creating Functional Food Ingredients with Health Benefits from Food Grains, their Processing Products, and By-products

Location: Functional Foods Research

Title: Prediction of bioactive composition in soybeans using NIR

item Singh, Mukti
item Berhow, Mark
item Liu, Sean

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 5/21/2019
Publication Date: 11/5/2019
Citation: Singh, M., Berhow, M.A., Liu, S.X. 2019. Prediction of bioactive composition in soybeans using NIR. Meeting Abstract. Cereals & Grains 19. November 3-5, 2019, Denver, CO.

Interpretive Summary:

Technical Abstract: Isoflavones and saponins are the major class of bioactives in soybeans that have been linked to cancer prevention and control. Rapid analytical techniques are needed to estimate their levels in soybeans as they arrive at the grain elevators and processing plants. We are evaluating NIRS as a suitable, rapid, nondestructive method to determine isoflavone composition in ground soybeans. Soybean samples (N >3000) were obtained the Agricultural Research Service soybean germplasm collection, and from several locations in United States over a five year period. The soybean samples were ground and scanned on near infrared spectrometers (NIRS) and analyzed by wet chemical methods for total isoflavone composition (genistein, daidzein and glycitein), and total saponin composition (A group-, B group- and DMPP-group). A subset selection of these samples was used to prepare NIRS calibrations. Selected preprocessing algorithms were applied to spectral data to minimize/eradicate noise or disturbance in the spectra. Partial Least Squares (PLS) regression analysis of preprocessed spectral data and wet chemistry data was used to develop models to predict individual sugars. The selection of a suitable calibration model was based on a high regression coefficient (R2), and lower standard error of calibration (SEC) values. Optimized PLS regression models were then used to predict validation sets. Reasonable predictions were obtained for isoflavones, however less than robust calibrations were obtained for the total saponins.