Caporaso Nicola, Whitworth Martin B, Grebby Stephen, Fisk Ian D
Campden BRI, Chipping Campden, Gloucestershire, GL55 6LD, UK.
Division of Food Sciences, University of Nottingham, Sutton Bonington Campus, LE12 5RD, UK.
J Food Eng. 2018 Jun;227:18-29. doi: 10.1016/j.jfoodeng.2018.01.009.
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.
高光谱成像（1000 - 2500纳米）用于在单个咖啡豆基础上快速预测完整生咖啡豆中的水分和总脂质含量。使用“推扫式”系统对来自多个种植地点的阿拉比卡和罗布斯塔咖啡豆样本进行扫描。对超立方体进行分割以选择单个咖啡豆，并测量每颗咖啡豆的平均光谱。采用偏最小二乘回归在单个咖啡豆（n = 320 - 350）上建立定量预测模型。这些模型表现出良好的性能，水分预测误差约为0.28%，脂质预测误差约为0.89%，可接受。本研究首次针对咖啡，特别是生咖啡豆，开发了基于高光谱成像的定量预测模型。此外，这是首次尝试使用单个完整咖啡豆建立此类模型。研究了咖啡豆之间的成分变异性，并可视化了单个咖啡豆内的脂肪和水分分布。这种快速、无损的方法可能对研究实验室、育种计划以及工业快速筛选具有重要应用价值。
