Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging
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...
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| Format: | Article |
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Elsevier
2018
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| Online Access: | https://eprints.nottingham.ac.uk/49235/ |
| _version_ | 1848797952416415744 |
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| author | Caporaso, Nicola Whitworth, Martin B. Grebby, Stephen Fisk, Ian D. |
| author_facet | Caporaso, Nicola Whitworth, Martin B. Grebby, Stephen Fisk, Ian D. |
| author_sort | Caporaso, Nicola |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:12:03Z |
| format | Article |
| id | nottingham-49235 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:12:03Z |
| publishDate | 2018 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-492352020-05-04T19:50:49Z https://eprints.nottingham.ac.uk/49235/ Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging Caporaso, Nicola Whitworth, Martin B. Grebby, Stephen Fisk, Ian D. 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. Elsevier 2018-06 Article PeerReviewed Caporaso, Nicola, Whitworth, Martin B., Grebby, Stephen and Fisk, Ian D. (2018) Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging. Journal of Food Engineering, 228 . pp. 18-29. ISSN 0260-8774 machine vision technology; coffee quality; chemical imaging; coffee fat; near-infrared spectroscopy; individual bean analysis https://www.sciencedirect.com/science/article/pii/S0260877418300219?via%3Dihub doi:10.1016/j.jfoodeng.2018.01.009 doi:10.1016/j.jfoodeng.2018.01.009 |
| spellingShingle | machine vision technology; coffee quality; chemical imaging; coffee fat; near-infrared spectroscopy; individual bean analysis Caporaso, Nicola Whitworth, Martin B. Grebby, Stephen Fisk, Ian D. Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging |
| title | Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging |
| title_full | Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging |
| title_fullStr | Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging |
| title_full_unstemmed | Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging |
| title_short | Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging |
| title_sort | rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging |
| topic | machine vision technology; coffee quality; chemical imaging; coffee fat; near-infrared spectroscopy; individual bean analysis |
| url | https://eprints.nottingham.ac.uk/49235/ https://eprints.nottingham.ac.uk/49235/ https://eprints.nottingham.ac.uk/49235/ |