Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging.
Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging.
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DOI：
10.1016/j.foodres.2017.12.031
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发表时间：
2018-04
期刊：
影响因子：
--
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(HSI)是一种用于食品部门的新技术，它能够实现对食品原料的快速非接触分析。HSI首次应用于单一种子水平的整个绿色咖啡豆，用于定量预测蔗糖、咖啡因和胡芦巴碱的含量。此外，对阿拉比卡和罗布斯塔咖啡的咖啡豆内成分分布进行了分析，样本来自12个国家，总共使用了260个样本。用反射HSI(980-2500 nm)扫描单个青咖啡豆，然后用参照法(高效液相-质谱仪)分析蔗糖、咖啡因和胡芦巴碱的浓度。利用偏最小二乘回归建立定量预测模型。蔗糖、咖啡因和胡芦巴碱在不同品种和产地之间存在很大差异，但同一批次的豆类中也存在差异。结果表明，蔗糖含量的估计对于筛选是可能的(R2=0.65；预测误差约为0.7%w/w咖啡，观测范围为~36.5%)，而最小二乘模型对咖啡因和胡芦巴碱的预测效果较好(R2=0.85和R2=100.82；对2.3w/w咖啡和1.1%w/w咖啡的预测误差分别为0.2%和0.1%)。预测误差主要用于实验室应用，可能应用于育种计划和食品工业的筛选目的。对于单个咖啡豆，咖啡成分的空间分布也被成功地可视化，这使得能够在单像素水平上绘制整个咖啡豆结构的分析物地图。近红外高光谱成像应用于绿豆的单一种子基础上。用高效液相色谱-质谱法分析了单豆中的蔗糖、咖啡因和胡芦巴碱。建立回归模型对咖啡成分进行量化。预测模型被应用于单像素以可视化复合分布。HSI被证明对单个咖啡豆的快速非破坏性筛选是有用的。
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. NIR hyperspectral imaging was applied to green coffee beans on a single seed basis. Sucrose, caffeine and trigonelline were analysed from single beans using HPLC-MS. PLS regression models were built to quantify for the coffee constituents. Prediction models were applied to single pixels to visualise the compound distribution. HSI was shown to be useful for rapid non-destructive screening of single coffee beans.
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