FAPD: An Astringency Threshold and Astringency Type Prediction Database for Flavonoid Compounds Based on Machine Learning

文献类型： 外文期刊

第一作者： Guo, Tianyang

作者： Guo, Tianyang;Yang, Zichen;Chen, Qiong;Zhao, Lei;Song, Huanlu;Pan, Fei;Cui, Zhiyong

作者机构：

关键词： astringency threshold; astringency type; flavonoid compounds; machine learning (ML); flavonoid astringency prediction database (FAPD)

期刊名称：JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY （ 影响因子：6.1； 五年影响因子：6.3 ）

ISSN： 0021-8561

年卷期： 2023 年

页码：

收录情况： SCI

摘要： 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 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.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序