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Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging

Caporaso, Nicola; Whitworth, Martin B.; Grebby, Stephen; Fisk, Ian D.

Authors

Nicola Caporaso

Martin B. Whitworth



Abstract

© 2018 The Authors Hyperspectral imaging (1000–2500 nm) was used for rapid prediction of moisture and total lipid content in intact green coffee beans on a single bean basis. Arabica and Robusta samples from several growing locations were scanned using a “push-broom” system. Hypercubes were segmented to select single beans, and average spectra were measured for each bean. Partial Least Squares regression was used to build quantitative prediction models on single beans (n = 320–350). The models exhibited good performance and acceptable prediction errors of ∼0.28% for moisture and ∼0.89% for lipids. This study represents the first time that HSI-based quantitative prediction models have been developed for coffee, and specifically green coffee beans. In addition, this is the first attempt to build such models using single intact coffee beans. The composition variability between beans was studied, and fat and moisture distribution were visualized within individual coffee beans. This rapid, non-destructive approach could have important applications for research laboratories, breeding programmes, and for rapid screening for industry.

Citation

Caporaso, N., Whitworth, M. B., Grebby, S., & Fisk, I. D. (2018). Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging. Journal of Food Engineering, 227, 18-29. https://doi.org/10.1016/j.jfoodeng.2018.01.009

Journal Article Type Article
Acceptance Date Jan 16, 2018
Online Publication Date Feb 3, 2018
Publication Date Jun 1, 2018
Deposit Date Jan 22, 2018
Publicly Available Date Mar 28, 2024
Journal Journal of Food Engineering
Print ISSN 0260-8774
Electronic ISSN 0260-8774
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 227
Pages 18-29
DOI https://doi.org/10.1016/j.jfoodeng.2018.01.009
Keywords machine vision technology; coffee quality; chemical imaging; coffee fat; near-infrared spectroscopy; individual bean analysis
Public URL https://nottingham-repository.worktribe.com/output/961221
Publisher URL https://www.sciencedirect.com/science/article/pii/S0260877418300219?via%3Dihub
Additional Information This article is maintained by: Elsevier; Article Title: Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging; Journal Title: Journal of Food Engineering; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.jfoodeng.2018.01.009; Content Type: article; Copyright: © 2018 The Authors. Published by Elsevier Ltd.

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