Discrimination of complex mixtures by a colorimetric sensor array: coffee aromas.
Discrimination of complex mixtures by a colorimetric sensor array: coffee aromas.
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DOI：
10.1021/ac902823w
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发表时间：
2010-03-01
影响因子：
7.4
通讯作者：
Suslick, Kenneth S.
中科院分区：
文献类型：
--
作者：
Suslick, Benjamin A.;Feng, Liang;Suslick, Kenneth S.
即使对现代分析技术来说，对复杂混合物的分析也是一项艰巨的挑战，区分非常相似的此类混合物的能力往往仍然是个问题。咖啡为这种高度多组分的系统提供了一个现成的原型。报道了一种低成本、灵敏的比色传感器阵列用于咖啡香气的检测和鉴定。传感器阵列的颜色变化被用作阵列响应的数字表示，并使用标准统计方法进行分析，包括主成分分析(PCA)和层次聚类分析(HCA)。主成分分析表明，传感器阵列具有非常高的维度，需要18个维度来定义90%的总方差。在对10种商业咖啡和对照进行的五次实验中，在55次试验中，没有观察到HCA分类的混淆或错误。此外，青咖啡豆烘焙过程中温度和时间的影响很容易观察和区分，分辨率分别优于10°C和5min。在实际应用中，比色传感器阵列在复杂系统分析中显示出极大的潜力，并为区分相似的复杂混合物提供了一种新的方法。
The analysis of complex mixtures presents a difficult challenge even for modern analytical techniques, and the ability to discriminate among closely similar such mixtures often remains problematic. Coffee provides a readily available archetype of such highly multicomponent systems. The use of a low-cost, sensitive colorimetric sensor array for the detection and identification of coffee aromas is reported. The color changes of the sensor array were used as a digital representation of the array response and analyzed with standard statistical methods, including principal component analysis (PCA) and hierarchical clustering analysis (HCA). PCA revealed that the sensor array has exceptionally high dimensionality with 18 dimensions required to define 90% of the total variance. In quintuplicate runs of 10 commercial coffees and controls, no confusions or errors in classification by HCA were observed in 55 trials. In addition, the effects of temperature and time in the roasting of green coffee beans were readily observed and distinguishable with a resolution better than 10 °C and 5 min, respectively. Colorimetric sensor arrays demonstrate excellent potential for complex systems analysis in real-world applications and provide a novel method for discrimination among closely similar complex mixtures.
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影响因子：
7.4
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通讯作者：
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作者：
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