ArticleMolecules (Basel, Switzerland)2020
Exploring the Characteristics of an Aroma-Blending Mixture by Investigating the Network of Shared Odors and the Molecular Features of Their Related Odorants.
Article in Molecules (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 19 citations in OpenAlex.
- Almond-like Aroma Formation of Acid Whey byJournal of agricultural and food chemistry · 2025Article
- Deep Learning for Odor Prediction on Aroma-Chemical Blends.ACS omega · 2025Article
- Pred-O3, a web server to predict molecules, olfactory receptors and odor relationships.Nucleic acids research · 2024Article
- Aroma Profile Development in Beer Fermented with Azacca, Idaho-7, and Sultana Hops.Molecules (Basel, Switzerland) · 2023Article
- Combining the Classification and Pharmacophore Approaches to Understand Homogeneous Olfactory Perceptions at Peripheral Level: Focus on Two Aroma Mixtures.Molecules (Basel, Switzerland) · 2023Article
- Analysis of the Ability to Produce Pleasant Aromas on Sour Whey and Buttermilk By-Products by MoldMolecules (Basel, Switzerland) · 2021Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
The perception of aroma mixtures is based on interactions beginning at the peripheral olfactory system, but the process remains poorly understood. The perception of a mixture of ethyl isobutyrate (Et-iB, strawberry-like odor) and ethyl maltol (Et-M, caramel-like odor) was investigated previously in both human and animal studies. In those studies, the binary mixture of Et-iB and Et-M was found to be configurally processed. In humans, the mixture was judged as more typical of a pineapple odor, similar to allyl hexanoate (Al-H, pineapple-like odor), than the odors of the individual components. To explore the key features of this aroma blend, we developed an in silico approach based on molecules having at least one of the odors-strawberry, caramel or pineapple. A dataset of 293 molecules and their related odors was built. We applied the notion of a "social network" to describe the network of the odors. Additionally, we explored the structural properties of the molecules in this dataset. The network of the odors revealed peculiar links between odors, while the structural study emphasized key characteristics of the molecules. The association between "strawberry" and "caramel" notes, as well as the structural diversity of the "strawberry" molecules, were notable. Such elements would be key to identifying potential odors/odorants to form aroma blends.
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