Analysis of a Commercial Red Wine Fermentation Dataset with a Wine Kinetic Model
The adoption of sensors to monitor wine fermentation enables the collection of large datasets that relate the initial juice chemistry, density and temperature patterns during fermentation to fermentation outcomes. Wine kinetic models are now being applied to commercial fermentations in real time to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Fermentation |
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| Online Access: | https://www.mdpi.com/2311-5637/11/1/4 |
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| author | James Nelson Robert Coleman Patrick Gravesen Michael Silacci Alaina Velasquez Kimberlee Marinelli Roger Boulton |
| author_facet | James Nelson Robert Coleman Patrick Gravesen Michael Silacci Alaina Velasquez Kimberlee Marinelli Roger Boulton |
| author_sort | James Nelson |
| collection | DOAJ |
| description | The adoption of sensors to monitor wine fermentation enables the collection of large datasets that relate the initial juice chemistry, density and temperature patterns during fermentation to fermentation outcomes. Wine kinetic models are now being applied to commercial fermentations in real time to identify abnormal or sluggish fermentations. In this work, 222 red wine fermentations from five harvests at two commercial wineries were evaluated by a wine fermentation model. The model parameters, initial juice chemistries and fermentation outcomes were analyzed for trends and relationships between them. While the fermentations with higher initial assimilable nitrogen concentrations had higher maximum fermentation rates, this did not guarantee successful fermentation outcomes in the tailing stage of the fermentation. Neither the initial, final, minimum and maximum temperatures, nor the initial pH, titratable acidity, measured yeast-assimilable nitrogen and primary amino nitrogen concentrations had any significant correlation with the maximum fermentation rate or successful completion of the fermentation. These results suggest that the initial juice-assimilable nitrogen measurements for these juices are of limited use in predicting slower and incomplete fermentation outcomes. |
| format | Article |
| id | doaj-art-acbd61b13fb24958892ffbfa84e1e28a |
| institution | Kabale University |
| issn | 2311-5637 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fermentation |
| spelling | doaj-art-acbd61b13fb24958892ffbfa84e1e28a2025-01-24T13:32:00ZengMDPI AGFermentation2311-56372024-12-01111410.3390/fermentation11010004Analysis of a Commercial Red Wine Fermentation Dataset with a Wine Kinetic ModelJames Nelson0Robert Coleman1Patrick Gravesen2Michael Silacci3Alaina Velasquez4Kimberlee Marinelli5Roger Boulton6Department of Viticulture and Enology, University of California, Davis, CA 95616, USADepartment of Viticulture and Enology, University of California, Davis, CA 95616, USABeringer Vineyards, Treasury Wine Estates, St. Helena, CA 94574, USAOpus One Winery, Oakville, CA 94562, USAOpus One Winery, Oakville, CA 94562, USAOpus One Winery, Oakville, CA 94562, USADepartment of Viticulture and Enology, University of California, Davis, CA 95616, USAThe adoption of sensors to monitor wine fermentation enables the collection of large datasets that relate the initial juice chemistry, density and temperature patterns during fermentation to fermentation outcomes. Wine kinetic models are now being applied to commercial fermentations in real time to identify abnormal or sluggish fermentations. In this work, 222 red wine fermentations from five harvests at two commercial wineries were evaluated by a wine fermentation model. The model parameters, initial juice chemistries and fermentation outcomes were analyzed for trends and relationships between them. While the fermentations with higher initial assimilable nitrogen concentrations had higher maximum fermentation rates, this did not guarantee successful fermentation outcomes in the tailing stage of the fermentation. Neither the initial, final, minimum and maximum temperatures, nor the initial pH, titratable acidity, measured yeast-assimilable nitrogen and primary amino nitrogen concentrations had any significant correlation with the maximum fermentation rate or successful completion of the fermentation. These results suggest that the initial juice-assimilable nitrogen measurements for these juices are of limited use in predicting slower and incomplete fermentation outcomes.https://www.mdpi.com/2311-5637/11/1/4wine fermentationmodelingincomplete and sluggish fermentationIndustry 4.0 |
| spellingShingle | James Nelson Robert Coleman Patrick Gravesen Michael Silacci Alaina Velasquez Kimberlee Marinelli Roger Boulton Analysis of a Commercial Red Wine Fermentation Dataset with a Wine Kinetic Model Fermentation wine fermentation modeling incomplete and sluggish fermentation Industry 4.0 |
| title | Analysis of a Commercial Red Wine Fermentation Dataset with a Wine Kinetic Model |
| title_full | Analysis of a Commercial Red Wine Fermentation Dataset with a Wine Kinetic Model |
| title_fullStr | Analysis of a Commercial Red Wine Fermentation Dataset with a Wine Kinetic Model |
| title_full_unstemmed | Analysis of a Commercial Red Wine Fermentation Dataset with a Wine Kinetic Model |
| title_short | Analysis of a Commercial Red Wine Fermentation Dataset with a Wine Kinetic Model |
| title_sort | analysis of a commercial red wine fermentation dataset with a wine kinetic model |
| topic | wine fermentation modeling incomplete and sluggish fermentation Industry 4.0 |
| url | https://www.mdpi.com/2311-5637/11/1/4 |
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