Non-Destructive Peat Analysis using Hyperspectral Imaging and Machine Learning
This addresses the environmental and efficiency challenges in whisky manufacturing, though it appears incremental as a feasibility study.
The paper tackled the problem of peat extraction's environmental impact in whisky production by conducting a feasibility study on non-destructive analysis using hyperspectral imaging, achieving up to 99.81% accuracy in predicting total phenol levels with SWIR data.
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%.