Functional Foods Research Unit, United States Department of Agriculture, Agricultural Research Service, National Center for Agricultural Utilization Research, 1815 N. University St., Peoria, IL 61604, United States(1).
Functional Foods Research Unit, United States Department of Agriculture, Agricultural Research Service, National Center for Agricultural Utilization Research, 1815 N. University St., Peoria, IL 61604, United States(1).
Food Chem. 2020 Jul 1;317:126373. doi: 10.1016/j.foodchem.2020.126373. Epub 2020 Feb 19.
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 (R), 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.
在 2012 年至 2016 年期间，从美国各地的生产地点获得了超过 3200 个离散的大豆样本。将研磨后的样品在近红外光谱仪（NIRS）上进行扫描，并通过高效液相色谱法（HPLC）分析总异黄酮和总皂苷组成以及总碳水化合物组成。使用多元线性回归（MLR）分析预处理后的光谱数据，以开发优化模型来预测异黄酮含量。选择合适的校准模型基于高回归系数（R）和较低的校准标准误差（SEC）值。对异黄酮进行了稳健的验证预测，但是对总皂苷的校准结果不够稳健。对碳水化合物组成的预测相关性不太强。NIRS 是一种快速、无损的方法，可用于测定研磨后的大豆中的异黄酮组成。通过快速获得的 NIRS 扫描，可以对大量大豆样品进行有用的异黄酮组成预测。
