Odor prediction of whiskies based on their molecular composition
Abstract Aroma compositions are usually complex mixtures of odor-active compounds exhibiting diverse molecular structures. Due to chemical interactions of these compounds in the olfactory system, assessing or even predicting the olfactory quality of such mixtures is a difficult task, not only for st...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Communications Chemistry |
| Online Access: | https://doi.org/10.1038/s42004-024-01373-2 |
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| _version_ | 1850102194824544256 |
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| author | Satnam Singh Doris Schicker Helen Haug Tilman Sauerwald Andreas T. Grasskamp |
| author_facet | Satnam Singh Doris Schicker Helen Haug Tilman Sauerwald Andreas T. Grasskamp |
| author_sort | Satnam Singh |
| collection | DOAJ |
| description | Abstract Aroma compositions are usually complex mixtures of odor-active compounds exhibiting diverse molecular structures. Due to chemical interactions of these compounds in the olfactory system, assessing or even predicting the olfactory quality of such mixtures is a difficult task, not only for statistical models, but even for trained assessors. Here, we combine fast automated analytical assessment tools with human sensory data of 11 experienced panelists and machine learning algorithms. Using 16 previously analyzed whisky samples (American or Scotch origin), we apply the linear classifier OWSum to distinguish the samples based on their detected molecules and to gain insights into the key molecular structure characteristics and odor descriptors for sample type. Moreover, we use OWSum and a Convolutional Neural Network (CNN) architecture to classify the five most relevant odor attributes of each sample and predict their sensory scores with promising accuracies (up to F1: 0.71, MCC: 0.68, ROCAUC: 0.78). The predictions outperform the inter-panelist agreement and thus demonstrate previously impossible data-driven sensory assessment in mixtures. |
| format | Article |
| id | doaj-art-86b476f478a0486184a7d5aa9a6d1121 |
| institution | DOAJ |
| issn | 2399-3669 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Chemistry |
| spelling | doaj-art-86b476f478a0486184a7d5aa9a6d11212025-08-20T02:39:48ZengNature PortfolioCommunications Chemistry2399-36692024-12-01711910.1038/s42004-024-01373-2Odor prediction of whiskies based on their molecular compositionSatnam Singh0Doris Schicker1Helen Haug2Tilman Sauerwald3Andreas T. Grasskamp4Department of Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVVDepartment of Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVVDepartment of Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVVDepartment of Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVVDepartment of Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVVAbstract Aroma compositions are usually complex mixtures of odor-active compounds exhibiting diverse molecular structures. Due to chemical interactions of these compounds in the olfactory system, assessing or even predicting the olfactory quality of such mixtures is a difficult task, not only for statistical models, but even for trained assessors. Here, we combine fast automated analytical assessment tools with human sensory data of 11 experienced panelists and machine learning algorithms. Using 16 previously analyzed whisky samples (American or Scotch origin), we apply the linear classifier OWSum to distinguish the samples based on their detected molecules and to gain insights into the key molecular structure characteristics and odor descriptors for sample type. Moreover, we use OWSum and a Convolutional Neural Network (CNN) architecture to classify the five most relevant odor attributes of each sample and predict their sensory scores with promising accuracies (up to F1: 0.71, MCC: 0.68, ROCAUC: 0.78). The predictions outperform the inter-panelist agreement and thus demonstrate previously impossible data-driven sensory assessment in mixtures.https://doi.org/10.1038/s42004-024-01373-2 |
| spellingShingle | Satnam Singh Doris Schicker Helen Haug Tilman Sauerwald Andreas T. Grasskamp Odor prediction of whiskies based on their molecular composition Communications Chemistry |
| title | Odor prediction of whiskies based on their molecular composition |
| title_full | Odor prediction of whiskies based on their molecular composition |
| title_fullStr | Odor prediction of whiskies based on their molecular composition |
| title_full_unstemmed | Odor prediction of whiskies based on their molecular composition |
| title_short | Odor prediction of whiskies based on their molecular composition |
| title_sort | odor prediction of whiskies based on their molecular composition |
| url | https://doi.org/10.1038/s42004-024-01373-2 |
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