期刊
JOURNAL OF FOOD ENGINEERING
卷 227, 期 -, 页码 18-29出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jfoodeng.2018.01.009
关键词
Machine vision technology; Coffee quality; Chemical imaging; Coffee fat; Near-infrared spectroscopy; Individual bean analysis
资金
- Biotechnology and Biological Sciences Research Council [BB/N021126/1]
- BBSRC [BB/N021126/1, BB/N020979/1, BB/N020979/2] Funding Source: UKRI
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 similar to 0.28% for moisture and similar to 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. (C) 2018 The Authors. Published by Elsevier Ltd.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据