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

朴素贝叶斯分类器 支持向量机 随机森林 人工智能 类黄酮 机器学习 计算机科学 涩的 公共化学 化学 模式识别(心理学) 食品科学 生物化学 品味 抗氧化剂
作者
Tianyang Guo,Fei Pan,Zhiyong Cui,Zichen Yang,Qiong Chen,Lei Zhao,Huanlu Song
出处
期刊:Journal of Agricultural and Food Chemistry [American Chemical Society]
卷期号:71 (9): 4172-4183 被引量:31
标识
DOI:10.1021/acs.jafc.2c08822
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 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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Akim应助体贴凌柏采纳,获得10
刚刚
zyq完成签到,获得积分10
1秒前
西瓜完成签到,获得积分10
2秒前
xshzhou完成签到,获得积分10
4秒前
苗条白枫完成签到 ,获得积分10
5秒前
一棵草完成签到,获得积分10
5秒前
内向的跳跳糖完成签到,获得积分10
5秒前
遇见飞儿完成签到,获得积分0
5秒前
cream完成签到,获得积分20
6秒前
6秒前
小薛完成签到,获得积分10
6秒前
7秒前
Cu_wx完成签到,获得积分10
7秒前
噜噜噜噜噜完成签到,获得积分10
9秒前
赵慧霞关注了科研通微信公众号
9秒前
炎魔之王拉格纳罗斯完成签到,获得积分10
10秒前
内向苡完成签到,获得积分10
11秒前
以筱发布了新的文献求助10
13秒前
bhkwxdxy完成签到,获得积分10
14秒前
悦耳虔纹完成签到 ,获得积分10
14秒前
xx完成签到,获得积分10
14秒前
大气灵枫完成签到,获得积分10
14秒前
妮妮完成签到,获得积分10
15秒前
17秒前
Struggle完成签到 ,获得积分10
18秒前
18秒前
秦兴虎完成签到,获得积分10
19秒前
Drew11完成签到,获得积分10
19秒前
风趣青槐完成签到,获得积分10
21秒前
科隆龙完成签到,获得积分10
22秒前
22秒前
饱满一手完成签到 ,获得积分10
22秒前
99完成签到,获得积分10
24秒前
枕星发布了新的文献求助10
24秒前
drlq2022完成签到,获得积分10
25秒前
王山完成签到,获得积分10
26秒前
自觉寒梦完成签到,获得积分10
27秒前
ding应助缥缈一刀采纳,获得10
27秒前
pakiorder发布了新的文献求助10
27秒前
专心搞学术完成签到,获得积分10
27秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038303
求助须知:如何正确求助?哪些是违规求助? 3576013
关于积分的说明 11374210
捐赠科研通 3305780
什么是DOI,文献DOI怎么找? 1819322
邀请新用户注册赠送积分活动 892672
科研通“疑难数据库(出版商)”最低求助积分说明 815029