Non-Destructive Peat Analysis using Hyperspectral Imaging and Machine Learning

Yijun Yan, Jinchang Ren, Barry Harrison, Oliver Lewis, Yinhe Li +1 more
5/3/2024
cs.CVcs.LGeess.IV

No confident code implementation found for this paper.

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%.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Near-Infrared Hyperspectral Imaging Applications in Food Analysis -- Improving Algorithms and Methodologies

77% match2025

Visible and Hyperspectral Imaging for Quality Assessment of Milk: Property Characterisation and Identification

76% match2026

Deep learning-based hyperspectral image reconstruction for quality assessment of agro-product

76% match2024

Comparative Analysis of Hyperspectral Image Reconstruction Using Deep Learning for Agricultural and Biological Applications

74% match2024

AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution

74% match2025

Hyperspectral Imaging for Identifying Foreign Objects on Pork Belly

74% match2025

Cite this paper

@article{yan2024nondestructive,
  title  = {Non-Destructive Peat Analysis using Hyperspectral Imaging and Machine Learning},
  author = {Yijun Yan and Jinchang Ren and Barry Harrison and Oliver Lewis and Yinhe Li and Ping Ma},
  year   = {2024},
  eprint = {2405.02191},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.02191v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2405.02191.svg)](https://codeofpaper.com/paper/2405.02191)

Discussion