Non-Destructive Peat Analysis using Hyperspectral Imaging and Machine Learning

TL;DR

This study explores non-destructive hyperspectral imaging, especially SWIR data, to analyze peat quality for whisky production, achieving high prediction accuracy and potentially reducing environmental impact.

Contribution

It demonstrates the effectiveness of hyperspectral imaging, particularly SWIR, for non-destructive peat analysis and phenol level prediction in whisky manufacturing.

Findings

SWIR data outperforms other spectral ranges in accuracy

Phenol levels can be predicted with up to 99.81% accuracy

Potential for reducing ecological disruption in peat extraction

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%.