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

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

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: Feb 3, 2018
• 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.