Non-Destructive Peat Analysis using Hyperspectral Imaging and Machine Learning
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917656427233280 |
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| author | Yan, Yijun Ren, Jinchang Harrison, Barry Lewis, Oliver Li, Yinhe Ma, Ping |
| author_facet | Yan, Yijun Ren, Jinchang Harrison, Barry Lewis, Oliver Li, Yinhe Ma, Ping |
| contents | Peat, a crucial component in whisky production, imparts distinctive and irreplaceable flavours to the final product. However, the extraction of peat disrupts ancient ecosystems and releases significant amounts of carbon, contributing to climate change. This paper aims to address this issue by conducting a feasibility study on enhancing peat use efficiency in whisky manufacturing through non-destructive analysis using hyperspectral imaging. Results show that shot-wave infrared (SWIR) data is more effective for analyzing peat samples and predicting total phenol levels, with accuracies up to 99.81%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02191 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Non-Destructive Peat Analysis using Hyperspectral Imaging and Machine Learning Yan, Yijun Ren, Jinchang Harrison, Barry Lewis, Oliver Li, Yinhe Ma, Ping Computer Vision and Pattern Recognition Machine Learning Image and Video Processing Peat, a crucial component in whisky production, imparts distinctive and irreplaceable flavours to the final product. However, the extraction of peat disrupts ancient ecosystems and releases significant amounts of carbon, contributing to climate change. This paper aims to address this issue by conducting a feasibility study on enhancing peat use efficiency in whisky manufacturing through non-destructive analysis using hyperspectral imaging. Results show that shot-wave infrared (SWIR) data is more effective for analyzing peat samples and predicting total phenol levels, with accuracies up to 99.81%. |
| title | Non-Destructive Peat Analysis using Hyperspectral Imaging and Machine Learning |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2405.02191 |