Rha Chan-Su, Jang Eun Kyu, Hong Yong Deog, Park Won Seok
AMOREPACIFIC R&D Center, Yongin 17074, Korea.
Gyeonggi-do Agricultural Research & Extension Services, Hwaseong 18388, Korea.
Metabolites. 2021 Dec 17;11(12):884. doi: 10.3390/metabo11120884.
Soybean (; SB) leaf (SL) is an abundant non-conventional edible resource that possesses value-adding bioactive compounds. We predicted the attributes of SB based on the metabolomes of an SL using targeted metabolomics. The SB was planted in two cities, and SLs were regularly obtained from the SB plant. Nine flavonol glycosides were purified from SLs, and a validated simultaneous quantification method was used to establish rapid separation by ultrahigh-performance liquid chromatography-mass detection. Changes in 31 targeted compounds were monitored, and the compounds were discriminated by various supervised machine learning (ML) models. Isoflavones, quercetin derivatives, and flavonol derivatives were discriminators for cultivation days, varieties, and cultivation sites, respectively, using the combined criteria of supervised ML models. The neural model exhibited higher prediction power of the factors with high fitness and low misclassification rates while other models showed lower. We propose that a set of phytochemicals of SL is a useful predictor for discriminating characteristics of edible plants.
大豆（；SB）叶（SL）是一种丰富的非常规食用资源，含有具有增值作用的生物活性化合物。我们使用靶向代谢组学基于SL的代谢组预测了SB的属性。大豆种植于两个城市，并定期从大豆植株上获取叶片。从大豆叶中纯化出9种黄酮醇苷，并采用经过验证的同时定量方法通过超高效液相色谱 - 质谱检测建立快速分离方法。监测了31种目标化合物的变化，并通过各种监督机器学习（ML）模型对这些化合物进行判别。使用监督ML模型的综合标准，异黄酮、槲皮素衍生物和黄酮醇衍生物分别是种植天数、品种和种植地点的判别指标。神经模型对具有高拟合度和低错误分类率的因素表现出更高的预测能力，而其他模型则较低。我们提出，一组大豆叶植物化学物质是区分食用植物特征的有用预测指标。
