甜蜜
计算机科学
机器学习
人工智能
质量(理念)
图层(电子)
生物系统
生化工程
化学
食品科学
工程类
品味
生物
认识论
哲学
有机化学
作者
Zhengfei Yang,Ran Xiao,Guo‐Li Xiong,Qinlu Lin,Ying Liang,Wenbin Zeng,Jie Dong,Dongsheng Cao
出处
期刊:Food Chemistry
[Elsevier]
日期:2021-09-28
卷期号:372: 131249-131249
被引量:15
标识
DOI:10.1016/j.foodchem.2021.131249
摘要
Nowadays, computational approaches have drawn more and more attention when exploring the relationship between sweetness and chemical structure instead of traditional experimental tests. In this work, we proposed a novel multi-layer sweetness evaluation system based on machine learning methods. It can be used to evaluate sweet properties of compounds with different chemical spaces and categories, including natural, artificial, carbohydrate, non-carbohydrate, nutritive and non-nutritive ones, suitable for different application scenarios. Furthermore, it provided quantitative predictions of sweetness. In addition, sweetness-related chemical basis and structure transforming rules were obtained by using molecular cloud and matched molecular pair analysis (MMPA) methods. This work systematically improved the data quality, explored the best machine learning algorithm and molecular characterizing strategy, and finally obtained robust models to establish a multi-layer prediction system (available at: https://github.com/ifyoungnet/ChemSweet). We hope that this study could facilitate food scientists with efficient screening and precise development of high-quality sweeteners.
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