Mixture product design based on molecular surface charge density distribution and machine learning

doi:10.11949/0438-1157.20190795

Abstract

Abstract:

Modern business pays increasing attentions to mixture products due to its adjustable characteristics. For the design methods towards such kinds of products, the development of model-based design methods is faster than others, because of its efficiency and wide application. However, the models for some properties, like odor and color, with acceptable accuracy or general application range are still not available. Therefore, an application methodology of machine learning (ML) with molecular surface charge density distribution (Sdescriptors) for mixture product design is proposed in this study, where descriptors are employed to represent the product and ML is responsible for correlating them to the target properties, for the purpose of designing product directly. Specifically, machine learning model is expected to predict Sdescriptors of candidate products according to the assigned property value, and ingredients are screened out using Euclidean-based method according to the predicted descriptors. Finally, the properties of the candidate mixtures and its ingredients are verified by experiments. This methodology is introduced using a case study of mixture substitution fragrance design for cis-3-hexenyl propionate and two mixture fragrances are obtained ultimately. The odor properties of mixtures and physicochemical properties of their components are similar to the target, which highlights the effective of the proposed method.

Key words: systems engineering, product design, neural network, molecular surface charge density distribution, mixture design, fragrance

摘要：

由于混合物性能的可调控性，当前市场对其关注与日俱增。对于这类产品，基于模型的设计方法由于具有高效性以及普适性，相较于其他产品设计方法得到了更快的发展。但是对于很多性质，如气味、颜色等，准确且普适的模型尚不可得。因此，本文提出了一种基于分子表面电荷密度分布描述符（S描述符）和机器学习模型的混合物设计方法，采用描述符表征产品、再通过机器学习模型将其与性质关联，直接用于混合物产品设计。具体地，根据给定的产品性质需求，机器学习模型直接预测/设计混合物产品的S描述符；然后以欧几里德距离为指标，在给定的数据库中筛选出S描述符满足要求的候选混合物组成。最后，对候选混合物及其组分性质进行实验验证，完成设计。本文以香精的混合替代物设计作为算例，设计得到丙酸叶醇酯的两种混合香精替代物，通过实验对混合物进行了验证。结果表明，混合替代物的气味及其组分的各理化性质均与丙酸叶醇酯相近，证实本文所提出方法的有效性。

关键词: 系统工程, 产品设计, 神经网络, 分子表面电荷密度分布, 混合物设计, 香精

CLC Number:

TQ 021.8

Haitao MAO, Lu WANG, Zhiying XU, Wancui XIE, Jian DU, Lei ZHANG. Mixture product design based on molecular surface charge density distribution and machine learning[J]. CIESC Journal, 2020, 71(S1): 282-292.

毛海涛, 王璐, 许志颖, 解万翠, 都健, 张磊. 基于分子表面电荷密度分布与机器学习的混合物设计方法研究[J]. 化工学报, 2020, 71(S1): 282-292.

Figures/Tables 13

Fig.1 Molecular surface charge density distribution spectrum and S descriptors integrated from the polar area of spectrum

Fig.2 Workflow for establishment of machine learning model

Fig.3 Workflow of mixed fragrance design

Table 1 Group contribution method and corresponding thresholds

组分性质	基团贡献法公式	阈值
$δ$ /MPa^1/2	$δ = 1000 H 298 V a p - R T V 298 m 12$	$δ ≥ 158.9$
$T b$ /K	$T b = 244.7889 l n ∑ i ∈ G n i T b, i$	$T b ≥ 333$
$T f$ /K	$T f = 150.0218 + ∑ i ∈ G n i T f, i$	$T f ≥ 323$
$K o / w$	$l n K o / w = 0.4876 + ∑ i ∈ G n i K o / w, i$	$l n K o / w ≤ 2.91$
$L C 50$ /(mol·L^-1)	$- l n L C 50 = 2.18 + ∑ i ∈ G n i L C 50, i$	$- l n L C 50 ≤ 3.70$

Table 1 Group contribution method and corresponding thresholds

组分性质	基团贡献法公式	阈值
$δ$ /MPa^1/2	$δ = 1000 H 298 V a p - R T V 298 m 12$	$δ ≥ 158.9$
$T b$ /K	$T b = 244.7889 l n ∑ i ∈ G n i T b, i$	$T b ≥ 333$
$T f$ /K	$T f = 150.0218 + ∑ i ∈ G n i T f, i$	$T f ≥ 323$
$K o / w$	$l n K o / w = 0.4876 + ∑ i ∈ G n i K o / w, i$	$l n K o / w ≤ 2.91$
$L C 50$ /(mol·L^-1)	$- l n L C 50 = 2.18 + ∑ i ∈ G n i L C 50, i$	$- l n L C 50 ≤ 3.70$

Table 2 Detected objections and detection limits of PEN3 E-nose

传感器名称	检测气味类型	检出限/ (mg/kg)
W1C	有机化合物	10
W5S	氮氧化物	1
W3C	氨类	10
W6S	氢气	0.1
W5C	烷烃与非极性有机化合物	1
W1S	甲烷	100
W1W	含硫有机化合物	1
W2S	酒精	100
W2W	无机硫	1
W3S	有机化合物以及脂肪族有机化合物	10

Fig.4 Average performances of validation and testing for IML models established by 10-folds cross validation

Fig.5 Prediction performances of inverse machine learning towards 24 testing samples

Table 3 First consumer needs of cis-3-hexenyl propionate

参数	数值	参数	数值
可食用	32.42	馊味	25.93
烘焙味	43.00	愉悦度	60.19
甜味	34.37	蒸气压/Pa	53.86
水果味	31.54	扩散系数/（m²/h）	0.16
花香味	29.95

Fig.6 Euclidean distance differences between S descriptors predicted by IML models and screened ingredients from Keller database for cis-3-hexenyl propionate substitution

Table 4 Results for substitution design of cis-3-hexenyl propionate

参数	丙酸叶醇酯	替代混合物1			替代混合物2
参数	丙酸叶醇酯	4-异丙基苯甲醇	左旋香芹酮	混合物偏差	2-甲基戊酸	2-乙基丁酸烯丙酯	混合物偏差
CAS No.	33467-74-2	536-60-7	6485-40-1	—	97-61-0	7493-69-8	—
体积分数	1	0.2	0.8	—	0.4	0.6	—
溶解度 $δ$ ^①(298 K,水)/(mg/L)	158.9	1687	367.1	472.18	15000	157.3	5935.48
沸点T_b^①(101.325 kPa) /K	453-455	512.66	499.47	47.11-49.11	468.36	449.96	2.32-4.32
闪点T_f^①/K	333	498.15	465.15	138.75	364.26	327.59	9.258
K_o/w^①	2.909	2.37	2.71	-0.267	1.8	2.972	-0.4058
LC₅₀^②/(mol·L^-1)	3.36	3.25	3.39	0.002	2.45	4.03	0.038

Table 4 Results for substitution design of cis-3-hexenyl propionate

参数	丙酸叶醇酯	替代混合物1			替代混合物2
参数	丙酸叶醇酯	4-异丙基苯甲醇	左旋香芹酮	混合物偏差	2-甲基戊酸	2-乙基丁酸烯丙酯	混合物偏差
CAS No.	33467-74-2	536-60-7	6485-40-1	—	97-61-0	7493-69-8	—
体积分数	1	0.2	0.8	—	0.4	0.6	—
溶解度 $δ$ ^①(298 K,水)/(mg/L)	158.9	1687	367.1	472.18	15000	157.3	5935.48
沸点T_b^①(101.325 kPa) /K	453-455	512.66	499.47	47.11-49.11	468.36	449.96	2.32-4.32
闪点T_f^①/K	333	498.15	465.15	138.75	364.26	327.59	9.258
K_o/w^①	2.909	2.37	2.71	-0.267	1.8	2.972	-0.4058
LC₅₀^②/(mol·L^-1)	3.36	3.25	3.39	0.002	2.45	4.03	0.038

Fig.7 Vapor pressure and diffusion coefficient for cis-3-hexenyl propionate and its substituted mixtures’ components of diverse temperatures

Table 5 Equipment and materials for substitution of cis-3-hexenyl propionate

试剂	说明
丙酸叶醇酯	北京迈瑞达科技有限公司，纯度>98%
2-甲基戊酸	北京迈瑞达科技有限公司，纯度>98%
2-乙基丁酸烯丙酯	北京迈瑞达科技有限公司，纯度>97%
4-异丙基苯甲醇	河南郑州阿尔法化工有限公司，纯度>99%
左旋香芹酮	河南郑州阿尔法化工有限公司，纯度>97%
95%乙醇	天津市富宇化工有限公司

Fig.8 Rader pictures of E-nose for cis-3-hexenyl propionate and its mixed fragrance substitutions

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