Statistical discrimination using different machine learning models reveals dissimilar key compounds of soybean leaves in targeted polyphenol-metric metabolomics in terms of traits and cultivation.

Soybean (SB) leaves (SLs) contain diverse flavonoids with health-promoting properties. To investigate the chemical constituents of SB and their correlations across phenotypes, growing periods, and environmental factors, a validated separation method for mass detection was used with targeted metabolomics. Thirty-six polyphenols (1 coumestrol, 5 flavones, 18 flavonols, and 12 isoflavones) were identified in SLs, 31 of which were quantified. Machine learning (ML) modelling was used to differentiate between the variety, bean color, growing period, and cultivation area and identify the key compounds responsible for these differences. The isoflavone and flavonol profiles were influenced by the growing period and cultivation area based on bootstrap forest modelling. The neural model showed the best predictive capacity for SL differences among the various ML models. Discriminant polyphenols can differ depending on the ML method applied; therefore, a cautious approach should be ensured when using statistical ML outputs, including orthogonal partial least squares discriminant analysis.

Supervised Statistical Learning Prediction of Soybean Varieties and Cultivation Sites Using Rapid UPLC-MS Separation, Method Validation, and Targeted Metabolomic Analysis of 31 Phenolic Compounds in the Leaves.

Soybean (Glycine max; 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.