基于反向机器学习的调香设计方法

王璐; 毛海涛; 张磊; 刘琳琳; 都健

doi:10.11949/0438-1157.20191015

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基于反向机器学习的调香设计方法

过程系统工程 | 更新时间：2026-07-24

- 基于反向机器学习的调香设计方法
- Inverse machine learning-based fragrance tuned design method
- 化工学报 2019年70卷第12期页码：4722-4729
- 作者机构：
  
  大连理工大学化工学院化工系统工程研究所，辽宁大连 116024
- 作者简介：
  
  王璐（1997—），女，硕士研究生，877907576@qq.com
  张磊（1986—），男，博士，副教授，keleiz@dlut.edu.cn
- 基金信息：
  
  国家自然科学基金项目(21808025)
- DOI：10.11949/0438-1157.20191015
  中图分类号： TQ 021.8
- 收稿：2019-09-09，
  
  修回：2019-09-16，
  
  网络首发：2020-06-09，
  
  纸质出版：2019-12-05
- 稿件说明：
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王璐, 毛海涛, 张磊, 刘琳琳, 都健. 基于反向机器学习的调香设计方法[J]. 化工学报, 2019, 70(12): 4722-4729

WANG Lu, MAO Haitao, ZHANG Lei, LIU Linlin, DU Jian. Inverse machine learning-based fragrance tuned design method[J]. CIESC Journal, 2019, 70(12): 4722-4729
王璐, 毛海涛, 张磊, 刘琳琳, 都健. 基于反向机器学习的调香设计方法[J]. 化工学报, 2019, 70(12): 4722-4729 DOI： 10.11949/0438-1157.20191015.

WANG Lu, MAO Haitao, ZHANG Lei, LIU Linlin, DU Jian. Inverse machine learning-based fragrance tuned design method[J]. CIESC Journal, 2019, 70(12): 4722-4729 DOI： 10.11949/0438-1157.20191015.

摘要

香精香料业务拥有数十亿美元的市场，而调香技术的发展丰富了人们的现代社会生活。提出了用于调香设计的反向机器学习模型，采用基于类导体屏蔽模型（COSMO）得到的分子表面电荷密度分布作为香精分子的结构描述符设计最终的调香产品。首先，识别香精属性并根据需求将属性转化为目标性质。然后，调节气味并建立了反向机器学习（inverse machine learning

IML）模型，其中输入变量为气味，输出变量为分子结构描述符，利用训练得到的IML模型，根据目标特性，预测出潜在产品的结构描述符。最后在指定的数据库中通过欧几里得距离法筛选出候选的调香混合物。本文还以两类调香算例为例，利用该框架进行了调香设计，并利用实验数据及气味雷达图对实验结果进行了验证。

Abstract

The business of fragrances has become a multibillion-dollar market

and the development of fragrance tuned technology enriches modern social life. In this study

the inverse machine learning model for fragrance tuned design is proposed. The molecular surface charge density distribution based on the conductor-like screening model (COSMO) is used as the structural descriptor of the fragrance molecule to design the final fragrance tuned product. First

the fragrance attributes are identified and transform attributes into target properties according to needs. Then

change odor scores and establish the Inverse Machine Learning (IML) models

in which the input variables are odors and the output variable is molecular structure descriptor. Based on the trained IML models

the structure descriptors of the potential product are predicted according to the target properties. Finally

the candidate tuned mixtures were screened out using Euclidean-based method in the specified database. In this paper

two types of fragrant examples are taken as examples. The framework is used to design the fragrance

and the experimental data and odor radar map are used to verify the experimental results.

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