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Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging
Journal of Food Engineering ( IF 5.3 ) Pub Date : 2018-06-01 , DOI: 10.1016/j.jfoodeng.2018.01.009 Nicola Caporaso 1, 2 , Martin B Whitworth 1 , Stephen Grebby 3 , Ian D Fisk 2
Journal of Food Engineering ( IF 5.3 ) Pub Date : 2018-06-01 , DOI: 10.1016/j.jfoodeng.2018.01.009 Nicola Caporaso 1, 2 , Martin B Whitworth 1 , Stephen Grebby 3 , Ian D Fisk 2
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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.
中文翻译：
通过高光谱成像快速预测单颗生咖啡豆的水分和脂质含量
高光谱成像（1000-2500 nm）用于快速预测单个咖啡豆中完整生咖啡豆的水分和总脂质含量。使用“推扫帚”系统扫描来自多个种植地点的阿拉比卡和罗布斯塔样品。将超立方体分割以选择单个豆子，并测量每个豆子的平均光谱。使用偏最小二乘回归建立单个豆类的定量预测模型（n = 320-350）。该模型表现出良好的性能，并且可接受的水分预测误差约为 0.28%，脂质预测误差约为 0.89%。这项研究首次针对咖啡，特别是生咖啡豆开发了基于 HSI 的定量预测模型。此外，这是首次尝试使用单个完整的咖啡豆来构建此类模型。研究了咖啡豆之间的成分差异，并将单个咖啡豆内的脂肪和水分分布可视化。这种快速、非破坏性的方法对于研究实验室、育种计划和工业快速筛选具有重要的应用。
更新日期：2018-06-01
中文翻译：
通过高光谱成像快速预测单颗生咖啡豆的水分和脂质含量
高光谱成像（1000-2500 nm）用于快速预测单个咖啡豆中完整生咖啡豆的水分和总脂质含量。使用“推扫帚”系统扫描来自多个种植地点的阿拉比卡和罗布斯塔样品。将超立方体分割以选择单个豆子，并测量每个豆子的平均光谱。使用偏最小二乘回归建立单个豆类的定量预测模型（n = 320-350）。该模型表现出良好的性能，并且可接受的水分预测误差约为 0.28%，脂质预测误差约为 0.89%。这项研究首次针对咖啡，特别是生咖啡豆开发了基于 HSI 的定量预测模型。此外，这是首次尝试使用单个完整的咖啡豆来构建此类模型。研究了咖啡豆之间的成分差异，并将单个咖啡豆内的脂肪和水分分布可视化。这种快速、非破坏性的方法对于研究实验室、育种计划和工业快速筛选具有重要的应用。
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