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DC Field | Value | Language |
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dc.contributor.author | Kimutai, Gibson | - |
dc.contributor.author | Ngenzi, Alexander | - |
dc.contributor.author | Ramkat, Rose | - |
dc.contributor.author | Said, Rutabayiro Ngoga | - |
dc.contributor.author | Förster, Anna | - |
dc.date.accessioned | 2022-11-16T08:58:38Z | - |
dc.date.available | 2022-11-16T08:58:38Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | https://doi.org/10.3390/data6030034 | - |
dc.identifier.uri | http://ir.mu.ac.ke:8080/jspui/handle/123456789/7093 | - |
dc.description.abstract | Tea is currently the most popular beverage after water. Tea contributes to the livelihood of more than 10 million people globally. There are several categories of tea, but black tea is the most popular, accounting for about 78% of total tea consumption. Processing of black tea involves the following steps: plucking, withering, crushing, tearing and curling, fermentation, drying, sorting, and packaging. Fermentation is the most important step in determining the final quality of the processed tea. Fermentation is a time-bound process and it must take place under certain temperature and humidity conditions. During fermentation, tea color changes from green to coppery brown to signify the attainment of optimum fermentation levels. These parameters are currently manually monitored. At present, there is only one existing dataset on tea fermentation images. This study makes a tea fermentation dataset available, composed of tea fermentation conditions and tea fermentation images. | en_US |
dc.language.iso | en | en_US |
dc.publisher | MDPI | en_US |
dc.subject | Tea fermentation | en_US |
dc.subject | Internet of things | en_US |
dc.title | A data descriptor for black tea fermentation dataset | en_US |
dc.type | Article | en_US |
Appears in Collections: | School of Biological and Physical Sciences |
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