Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging
Hyperspectral imaging (HSI) is a novel technology for the food sector that enables rapid non-contact analysis of food materials. HSI was applied for the first time to whole green coffee beans, at a single seed level, for quantitative prediction of sucrose, caffeine and trigonelline content. In addit...
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| Format: | Article |
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Elsevier
2018
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| Online Access: | https://eprints.nottingham.ac.uk/48994/ |
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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 (HSI) is a novel technology for the food sector that enables rapid non-contact analysis of food materials. HSI was applied for the first time to whole green coffee beans, at a single seed level, for quantitative prediction of sucrose, caffeine and trigonelline content. In addition, the intra-bean distribution of coffee constituents was analysed in Arabica and Robusta coffees on a large sample set from 12 countries, using a total of 260 samples. Individual green coffee beans were scanned by reflectance HSI (980–2500 nm) and then the concentration of sucrose, caffeine and trigonelline analysed with a reference method (HPLC-MS). Quantitative prediction models were subsequently built using Partial Least Squares (PLS) regression. Large variations in sucrose, caffeine and trigonelline were found between different species and origin, but also within beans from the same batch. It was shown that estimation of sucrose content is possible for screening purposes (R2 = 0.65; prediction error of ~ 0.7% w/w coffee, with observed range of ~ 6.5%), while the performance of the PLS model was better for caffeine and trigonelline prediction (R2 = 0.85 and R2 = 0.82, respectively; prediction errors of 0.2 and 0.1%, on a range of 2.3 and 1.1% w/w coffee, respectively). The prediction error is acceptable mainly for laboratory applications, with the potential application to breeding programmes and for screening purposes for the food industry. The spatial distribution of coffee constituents was also successfully visualised for single beans and this enabled mapping of the analytes across the bean structure at single pixel level. |
| first_indexed | 2025-11-14T20:11:11Z |
| format | Article |
| id | nottingham-48994 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:11:11Z |
| publishDate | 2018 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-489942020-05-04T19:51:46Z https://eprints.nottingham.ac.uk/48994/ Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging Caporaso, Nicola Whitworth, Martin B. Grebby, Stephen Fisk, Ian D. Hyperspectral imaging (HSI) is a novel technology for the food sector that enables rapid non-contact analysis of food materials. HSI was applied for the first time to whole green coffee beans, at a single seed level, for quantitative prediction of sucrose, caffeine and trigonelline content. In addition, the intra-bean distribution of coffee constituents was analysed in Arabica and Robusta coffees on a large sample set from 12 countries, using a total of 260 samples. Individual green coffee beans were scanned by reflectance HSI (980–2500 nm) and then the concentration of sucrose, caffeine and trigonelline analysed with a reference method (HPLC-MS). Quantitative prediction models were subsequently built using Partial Least Squares (PLS) regression. Large variations in sucrose, caffeine and trigonelline were found between different species and origin, but also within beans from the same batch. It was shown that estimation of sucrose content is possible for screening purposes (R2 = 0.65; prediction error of ~ 0.7% w/w coffee, with observed range of ~ 6.5%), while the performance of the PLS model was better for caffeine and trigonelline prediction (R2 = 0.85 and R2 = 0.82, respectively; prediction errors of 0.2 and 0.1%, on a range of 2.3 and 1.1% w/w coffee, respectively). The prediction error is acceptable mainly for laboratory applications, with the potential application to breeding programmes and for screening purposes for the food industry. The spatial distribution of coffee constituents was also successfully visualised for single beans and this enabled mapping of the analytes across the bean structure at single pixel level. Elsevier 2018-04 Article PeerReviewed Caporaso, Nicola, Whitworth, Martin B., Grebby, Stephen and Fisk, Ian D. (2018) Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging. Food Research International, 106 . pp. 193-203. ISSN 1873-7145 Hyperspectral chemical imaging; NIR chemical mapping; Single seed variability; Coffee sugars; Coffee chemistry; Caffeine http://www.sciencedirect.com/science/article/pii/S0963996917308852 doi:10.1016/j.foodres.2017.12.031 doi:10.1016/j.foodres.2017.12.031 |
| spellingShingle | Hyperspectral chemical imaging; NIR chemical mapping; Single seed variability; Coffee sugars; Coffee chemistry; Caffeine Caporaso, Nicola Whitworth, Martin B. Grebby, Stephen Fisk, Ian D. Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging |
| title | Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging |
| title_full | Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging |
| title_fullStr | Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging |
| title_full_unstemmed | Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging |
| title_short | Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging |
| title_sort | non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging |
| topic | Hyperspectral chemical imaging; NIR chemical mapping; Single seed variability; Coffee sugars; Coffee chemistry; Caffeine |
| url | https://eprints.nottingham.ac.uk/48994/ https://eprints.nottingham.ac.uk/48994/ https://eprints.nottingham.ac.uk/48994/ |