Prediction of coffee aroma from single roasted coffee beans by hyperspectral imaging.
Prediction of coffee aroma from single roasted coffee beans by hyperspectral imaging.
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DOI：
10.1016/j.foodchem.2021.131159
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发表时间：
2022-03-01
期刊：
影响因子：
8.8
通讯作者：
Fisk ID
中科院分区：
文献类型：
--
作者：
Caporaso N;Whitworth MB;Fisk ID
应用高光谱成像（HSI）技术预测烘焙咖啡的香气特征。用气相色谱-质谱分析了单个烘焙咖啡豆的HSI和香气。PLS模型成功地预测了单个咖啡豆的挥发性香气化合物。咖啡豆被成功地分离成两批不同的香气。咖啡的香气对消费者的喜好至关重要，并使咖啡的价格差异化。本研究采用固相微萃取-气相色谱-质谱法和气相色谱-嗅觉法测定了高光谱成像（1000-2500 nm）对单一烘焙咖啡豆挥发性化合物的预测。建立了不同挥发性化合物和化学类别的偏最小二乘回归模型。选定的关键香气化合物预测得足够好，可以进行快速筛选（R2大于0.7，性能偏差比（RPD）大于1.5），并且对化合物类别（例如醛类和吡嗪类）的预测也得到了改进（R2 ~ 0.8, RPD ~ 1.9）。为了证明这种方法，通过HSI将咖啡豆成功地分离成具有不同水平吡嗪（烟熏）或醛（甜）的原型批次。这与工业相关，因为它将为质量评估提供新的快速工具，有机会了解和减少生产和烘焙过程中的异质性，并最终提供工具来定义和实现新的咖啡风味。
This paper applied hyperspectral imaging (HSI) to predict roasted coffee aroma profile. Individual roast coffee beans were analysed by HSI and aroma by GC–MS. PLS models successfully predicted volatile aroma compounds in single coffee beans. Beans were successfully segregated into two batches with different aroma profiles. Coffee aroma is critical for consumer liking and enables price differentiation of coffee. This study applied hyperspectral imaging (1000–2500 nm) to predict volatile compounds in single roasted coffee beans, as measured by Solid Phase Micro Extraction-Gas Chromatography-Mass Spectrometry and Gas Chromatography-Olfactometry. Partial least square (PLS) regression models were built for individual volatile compounds and chemical classes. Selected key aroma compounds were predicted well enough to allow rapid screening (R2 greater than 0.7, Ratio to Performance Deviation (RPD) greater than 1.5), and improved predictions were achieved for classes of compounds - e.g. aldehydes and pyrazines (R2 ∼ 0.8, RPD ∼ 1.9). To demonstrate the approach, beans were successfully segregated by HSI into prototype batches with different levels of pyrazines (smoky) or aldehydes (sweet). This is industrially relevant as it will provide new rapid tools for quality evaluation, opportunities to understand and minimise heterogeneity during production and roasting and ultimately provide the tools to define and achieve new coffee flavour profiles.
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影响因子：
5.5
作者：
Caporaso N;Whitworth MB;Grebby S;Fisk ID
通讯作者：
Fisk ID
影响因子：
4.1
作者：
Genovese, Alessandro;Caporaso, Nicola;Sacchi, Raffaele
通讯作者：
Sacchi, Raffaele
DOI：
10.1016/j.foodres.2018.03.077
发表时间：
2018-06
期刊：
Food research international (Ottawa, Ont.)
影响因子：
--
作者：
Caporaso N;Whitworth MB;Cui C;Fisk ID
通讯作者：
Fisk ID
影响因子：
6.1
作者：
Davey, Mark W.;Saeys, Wouter;Keulemans, Johan
通讯作者：
Keulemans, Johan
影响因子：
5
作者：
Chen, Shih-Yu;Chang, Chuan-Yu;Lien, Chou-Tien
通讯作者：
Lien, Chou-Tien