Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging
| Autor: | Ian D. Fisk, Stephen Grebby, Nicola Caporaso, M.B. Whitworth |
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| Rok vydání: | 2018 |
| Předmět: |
2. Zero hunger
Chemical imaging Coffee fat Moisture 010401 analytical chemistry Hyperspectral imaging 04 agricultural and veterinary sciences Machine vision technology 040401 food science 01 natural sciences Article 0104 chemical sciences Coffee quality Moisture distribution Horticulture 0404 agricultural biotechnology Near-infrared spectroscopy Individual bean analysis Lipid content Partial least squares regression Green coffee Food Science Mathematics |
| Zdroj: | Journal of Food Engineering |
| ISSN: | 0260-8774 |
| DOI: | 10.1016/j.jfoodeng.2018.01.009 |
| Popis: | 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. Graphical abstract Image 1 Highlights • Intact single green coffee beans were analysed for their moisture and fat content by HSI. • NIR Hyperspectral imaging was applied to develop PLS calibrations for these constituents. • The PLSR performance showed a performance comparable with traditional NIR instrumentation. • A classification model was successfully applied by PLS-DA to discriminate Arabica vs Robusta. |
| Databáze: | OpenAIRE |
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