Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging.
Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging.
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DOI：
10.1016/j.jfoodeng.2018.01.009
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发表时间：
2018-06
影响因子：
5.5
通讯作者：
Fisk ID
中科院分区：
文献类型：
--
作者：
Caporaso N;Whitworth MB;Grebby S;Fisk ID
高光谱成像（1000-2500 nm）用于快速预测完整的绿色咖啡豆中的水分和总脂质含量在单个豆的基础上。阿拉比卡和罗布斯塔样品从几个生长地点进行了扫描使用“推扫帚”系统。将超立方体分段以选择单个豆，并且测量每个豆的平均光谱。采用偏最小二乘回归建立单粒菜豆（n = 320-350）的定量预测模型。该模型表现出良好的性能和可接受的预测误差为0.28%的水分和0.89%的脂类。这项研究代表了第一次，基于HSI的定量预测模型已经开发的咖啡，特别是绿色咖啡豆。此外，这是首次尝试使用单个完整的咖啡豆构建此类模型。研究了咖啡豆之间的组成差异，并在单个咖啡豆内可视化脂肪和水分分布。这种快速、非破坏性的方法可能在研究实验室、育种计划和工业快速筛选方面有重要的应用。通过HSI分析完整的单个绿色咖啡豆的水分和脂肪含量。近红外高光谱成像应用于开发这些成分的PLS校准。PLSR的性能表现出与传统NIR仪器相当的性能。应用PLS-DA建立的分类模型成功地对阿拉比卡和罗布斯塔进行了判别。
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. 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.
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影响因子：
6.1
作者：
Keidel, Anke;von Stetten, David;Hildebrandt, Peter
通讯作者：
Hildebrandt, Peter
影响因子：
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Khodabux, Khalil;Sophia, Maria;Rondeau, Philippe
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影响因子：
6.2
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Esteban-Díez, I;González-Sáiz, JM;Pizarro, C
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Pizarro, C
影响因子：
4.3
作者：
Romano, Raffaele;Santini, Antonello;Ritieni, Alberto
通讯作者：
Ritieni, Alberto
影响因子：
5.5
作者：
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通讯作者：
Ngadi, Michael