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葡萄酒中多种香气成分气味阈值特性的化学计量学建模

Chemometric modeling of odor threshold property of diverse aroma components of wine.

作者信息

Ojha Probir Kumar, Roy Kunal

机构信息

Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University Kolkata 700 032 India

出版信息

RSC Adv. 2018 Jan 25;8(9):4750-4760. doi: 10.1039/c7ra12295k. eCollection 2018 Jan 24.

DOI:10.1039/c7ra12295k
PMID:35557995
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9092618/
Abstract

We have modelled here odor threshold properties (OTP) of various aroma components present in different types of wine using quantitative structure-property relationship (QSPR) studies employing both two-dimensional and three-dimensional descriptors. The aim has been to identify the molecular properties essential for lowering the OTP. We have applied different variable selection strategies to select the most relevant descriptors prior to the development of the final partial least squares (PLS) regression model, which was validated extensively using different validation metrics in terms of acceptability and predictivity of the model for enhancing confidence in QSPR predictions. Using the developed PLS model, we have also predicted the "composite" OTP of different types of wine using the "composite" descriptor values based on individual components according to the PLS model and the results were well corroborated with the observations reported by Wang [, 2017, , 41-50]. The developed model may guide us to understand the dependence of the odor quality of different types of wines obtained under different manufacturing conditions on their aroma constituents.

摘要

我们在此使用二维和三维描述符，通过定量结构-性质关系（QSPR）研究，对不同类型葡萄酒中存在的各种香气成分的气味阈值特性（OTP）进行了建模。目的是确定降低OTP所必需的分子特性。在开发最终的偏最小二乘（PLS）回归模型之前，我们应用了不同的变量选择策略来选择最相关的描述符，该模型使用不同的验证指标在模型的可接受性和预测性方面进行了广泛验证，以增强对QSPR预测的信心。使用开发的PLS模型，我们还根据PLS模型，使用基于各个成分的“复合”描述符值预测了不同类型葡萄酒的“复合”OTP，结果与Wang [, 2017, , 41 - 50]报道的观察结果得到了很好的证实。开发的模型可以指导我们理解在不同制造条件下获得的不同类型葡萄酒的气味质量对其香气成分的依赖性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebbb/9092618/090214ff209b/c7ra12295k-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebbb/9092618/f88a3f5992c8/c7ra12295k-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebbb/9092618/12690f9f9ff0/c7ra12295k-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebbb/9092618/8788a87203d7/c7ra12295k-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebbb/9092618/090214ff209b/c7ra12295k-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebbb/9092618/f88a3f5992c8/c7ra12295k-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebbb/9092618/12690f9f9ff0/c7ra12295k-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebbb/9092618/8788a87203d7/c7ra12295k-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebbb/9092618/090214ff209b/c7ra12295k-f4.jpg

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