Non-Destructive Peat Analysis using Hyperspectral Imaging and Machine Learning
- URL: http://arxiv.org/abs/2405.02191v1
- Date: Fri, 3 May 2024 15:47:07 GMT
- Title: Non-Destructive Peat Analysis using Hyperspectral Imaging and Machine Learning
- Authors: Yijun Yan, Jinchang Ren, Barry Harrison, Oliver Lewis, Yinhe Li, Ping Ma,
- Abstract summary: Peat, a crucial component in whisky production, imparts distinctive and irreplaceable flavours to the final product.
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%.
- Score: 4.949467670284275
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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%.
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