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Unraveling ingredients in complex mixtures by chromatographic spectrum recognition: Application to perfume deformulation
Flavour and Fragrance Journal ( IF 2.6 ) Pub Date : 2020-01-14 , DOI: 10.1002/ffj.3564
Clément Truan 1, 2 , Christophe Peres 3 , Erwan Engel 1
Affiliation  

In the past 40 years, progress in GC‐MS and GC‐FID has lent the modern perfume industry powerful tools for determining raw ingredients in fragrances, which is still often manual and based on molecular markers thus subjective and unreliable due to variability and chromatographic challenges. This article presents an alternative approach by introducing the concept of chromatographic spectrum. This approach was evaluated on a commercial database containing molecular composition of 4106 perfumery ingredients and on real and simulated mixtures. Five hundred and seven out of 565 database ingredients classes were differentiated by their chromatographic spectrum against 164 with distinctive markers. The 5 ingredients of a real Eau de Cologne mixture were identified, and their proportions estimated with less than 12% relative error for 4 of them. The usefulness of chromatographic spectra in a deformulation support algorithm is discussed based on the deformulation of 210 simulated mixtures of 5, 10, and 15 ingredients.

中文翻译:

通过色谱光谱识别来解开复杂混合物中的成分:在香水变形中的应用

在过去的40年中,GC-MS和GC-FID的进步为现代香水行业提供了强大的工具,可用于确定香水中的原料,该工具通常仍是手动操作,并且基于分子标记,因此由于可变性和色谱挑战而主观且不可靠。本文通过介绍色谱光谱概念介绍了一种替代方法。在包含4106种香料成分分子组成的商业数据库以及真实和模拟混合物上评估了该方法。565种数据库成分类别中的570种通过色谱图谱与具有独特标记的164种色谱图谱进行了区分。确定了真正的科隆淡香水混合物中的5种成分,并估计其中4种成分的比例相对误差小于12%。
更新日期:2020-01-14
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