Chemical Diversity of UK-Grown Tea Explored Using Metabolomics and Machine Learning
Background/Objectives: Dartmoor Estate Tea plantation in Devon, UK, is renowned for its unique microclimate and varied soil conditions, which contribute to the distinctive flavours and chemical profiles of tea. The chemical diversity of fresh leaf samples from various garden locations was explored w...
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2025-01-01
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| author | Amanda J. Lloyd Alina Warren-Walker Jasen Finch Jo Harper Kathryn Bennet Alison Watson Laura Lyons Pilar Martinez Martin Thomas Wilson Manfred Beckmann |
| author_facet | Amanda J. Lloyd Alina Warren-Walker Jasen Finch Jo Harper Kathryn Bennet Alison Watson Laura Lyons Pilar Martinez Martin Thomas Wilson Manfred Beckmann |
| author_sort | Amanda J. Lloyd |
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| description | Background/Objectives: Dartmoor Estate Tea plantation in Devon, UK, is renowned for its unique microclimate and varied soil conditions, which contribute to the distinctive flavours and chemical profiles of tea. The chemical diversity of fresh leaf samples from various garden locations was explored within the plantation. Methods: Fresh leaf, which differed by location, cultivar, time of day, and variety, was analysed using Flow Infusion Electrospray Ionisation Mass Spectrometry (FIE-MS). Results: Random forest classification revealed no significant differences between Georgian N2 cultivar garden locations. However, a significant degree of variability was observed within four tri-clonal variants (Tocklai Variety) with TV9 exhibiting greater similarity to the Georgian N2 cultivar compared to TV8 and TV11, while TV11 was found to be most like TV1. The intraclass variability in leaf composition was similar between the varieties. We explored the metabolic changes over the day in one variant (<i>Camellia assamica</i> Masters), yielding a model with a significant R<sup>2</sup> value of 0.617 (<i>p</i> < 0.01, 3000 permutations). Starch and sucrose metabolism was found to be significant where the abundance of these chemical features increased throughout the day and then began to decrease at night. Conclusions: This research highlights the complex interplay of cultivars, geographical location, and temporal factors on the chemical composition of tea. It provides insightful data on the metabolic pathways influencing tea cultivation and production and underscores the importance of these variables in determining the final chemical profile and organoleptic characteristics of tea products. |
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| institution | Kabale University |
| issn | 2218-1989 |
| language | English |
| publishDate | 2025-01-01 |
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| series | Metabolites |
| spelling | doaj-art-bcdd6fa5d9414afea705193ea5388fb22025-01-24T13:41:18ZengMDPI AGMetabolites2218-19892025-01-011515210.3390/metabo15010052Chemical Diversity of UK-Grown Tea Explored Using Metabolomics and Machine LearningAmanda J. Lloyd0Alina Warren-Walker1Jasen Finch2Jo Harper3Kathryn Bennet4Alison Watson5Laura Lyons6Pilar Martinez Martin7Thomas Wilson8Manfred Beckmann9Department of Life Sciences, Aberystwyth University, Aberystwyth SY23 3DA, UKDepartment of Life Sciences, Aberystwyth University, Aberystwyth SY23 3DA, UKDepartment of Life Sciences, Aberystwyth University, Aberystwyth SY23 3DA, UKDartmoor Estate Tea, Furzeleigh Farm, Ashburton, Newton Abbot TQ13 7JL, UKDartmoor Estate Tea, Furzeleigh Farm, Ashburton, Newton Abbot TQ13 7JL, UKDepartment of Life Sciences, Aberystwyth University, Aberystwyth SY23 3DA, UKDepartment of Life Sciences, Aberystwyth University, Aberystwyth SY23 3DA, UKDepartment of Life Sciences, Aberystwyth University, Aberystwyth SY23 3DA, UKDepartment of Life Sciences, Aberystwyth University, Aberystwyth SY23 3DA, UKDepartment of Life Sciences, Aberystwyth University, Aberystwyth SY23 3DA, UKBackground/Objectives: Dartmoor Estate Tea plantation in Devon, UK, is renowned for its unique microclimate and varied soil conditions, which contribute to the distinctive flavours and chemical profiles of tea. The chemical diversity of fresh leaf samples from various garden locations was explored within the plantation. Methods: Fresh leaf, which differed by location, cultivar, time of day, and variety, was analysed using Flow Infusion Electrospray Ionisation Mass Spectrometry (FIE-MS). Results: Random forest classification revealed no significant differences between Georgian N2 cultivar garden locations. However, a significant degree of variability was observed within four tri-clonal variants (Tocklai Variety) with TV9 exhibiting greater similarity to the Georgian N2 cultivar compared to TV8 and TV11, while TV11 was found to be most like TV1. The intraclass variability in leaf composition was similar between the varieties. We explored the metabolic changes over the day in one variant (<i>Camellia assamica</i> Masters), yielding a model with a significant R<sup>2</sup> value of 0.617 (<i>p</i> < 0.01, 3000 permutations). Starch and sucrose metabolism was found to be significant where the abundance of these chemical features increased throughout the day and then began to decrease at night. Conclusions: This research highlights the complex interplay of cultivars, geographical location, and temporal factors on the chemical composition of tea. It provides insightful data on the metabolic pathways influencing tea cultivation and production and underscores the importance of these variables in determining the final chemical profile and organoleptic characteristics of tea products.https://www.mdpi.com/2218-1989/15/1/52<i>Camellia sinensis</i> L.Flow Infusion Electrospray Ionisation Mass Spectrometry (FIE-MS)metabolomicsrandom forest classificationcultivarsgeographical location |
| spellingShingle | Amanda J. Lloyd Alina Warren-Walker Jasen Finch Jo Harper Kathryn Bennet Alison Watson Laura Lyons Pilar Martinez Martin Thomas Wilson Manfred Beckmann Chemical Diversity of UK-Grown Tea Explored Using Metabolomics and Machine Learning Metabolites <i>Camellia sinensis</i> L. Flow Infusion Electrospray Ionisation Mass Spectrometry (FIE-MS) metabolomics random forest classification cultivars geographical location |
| title | Chemical Diversity of UK-Grown Tea Explored Using Metabolomics and Machine Learning |
| title_full | Chemical Diversity of UK-Grown Tea Explored Using Metabolomics and Machine Learning |
| title_fullStr | Chemical Diversity of UK-Grown Tea Explored Using Metabolomics and Machine Learning |
| title_full_unstemmed | Chemical Diversity of UK-Grown Tea Explored Using Metabolomics and Machine Learning |
| title_short | Chemical Diversity of UK-Grown Tea Explored Using Metabolomics and Machine Learning |
| title_sort | chemical diversity of uk grown tea explored using metabolomics and machine learning |
| topic | <i>Camellia sinensis</i> L. Flow Infusion Electrospray Ionisation Mass Spectrometry (FIE-MS) metabolomics random forest classification cultivars geographical location |
| url | https://www.mdpi.com/2218-1989/15/1/52 |
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