Astringency is a puckering or velvety sensation mainly derived from flavonoid compounds in food. The traditional experimental approach for astringent compound discovery was labor-intensive and cost-consuming, while machine learning (ML) can greatly accelerate this procedure. Herein, we propose the Flavonoid Astringency Prediction Database (FAPD) based on ML. First, the Molecular Fingerprint Similarities (MFSs) and thresholds of flavonoid compounds were hierarchically clustering analyzed. For the astringency threshold prediction, four regressions models (i.e., Gaussian Process Regression (GPR), Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosted Decision Tree (GBDT)) were established, and the best model was RF which was interpreted by the SHapley Additive exPlanations (SHAP) approach. For the astringency type prediction, six classification models (i.e., RF, GBDT, Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Stochastic Gradient Descent (SGD)) were established, and the best model was SGD. Furthermore, over 1200 natural flavonoid compounds were discovered and built into the customized FAPD. In FAPD, the astringency thresholds were achieved by RF; the astringency types were distinguished by SGD, and the real and predicted astringency types were verified by t-Distributed Stochastic Neighbor Embedding (t-SNE). Therefore, ML models can be used to predict the astringency threshold and astringency type of flavonoid compounds, which provides a new paradigm to research the molecular structure-flavor property relationship of food components.
Keywords: astringency threshold; astringency type; flavonoid astringency prediction database (FAPD); flavonoid compounds; machine learning (ML).