Zhang Chen, Bailey Daniel P, Suslick Kenneth S
Department of Chemistry, University of Illinois at Urbana-Champaign, 600 South Mathews Avenue, Urbana, Illinois 61801, USA.
J Agric Food Chem. 2006 Jul 12;54(14):4925-31. doi: 10.1021/jf060110a.
Eighteen commercial beers have been analyzed in both liquid and gas phases using colorimetric sensor arrays made from selected chemically responsive dyes printed on a hydrophobic membrane. Digital imaging of the dye array before and after exposure to the complex analytes in either the liquid phase or the head-gas provides a color change profile as a unique fingerprint for the specific analyte. The digital data libraries generated were analyzed using statistical and chemometric methods, including principal component analysis (PCA) and hierarchical clustering analysis (HCA). In either liquid- or gas-phase experiments, facile identification of specific beers was achieved using comparison of the color change profiles; using HCA statistical analysis the error rate of identification was <3%. Differentiation between even very similar beers proved to be straightforward. In addition, differentiation of pristine beer from the effects of watering or decarbonation proved to be possible. These results suggest that colorimetric sensor arrays may prove to be useful for quality assurance/quality control applications of beers and perhaps other beverages.
使用由印刷在疏水膜上的选定化学响应染料制成的比色传感器阵列,对18种市售啤酒的液相和气相进行了分析。在液相或顶空气体中,染料阵列暴露于复杂分析物之前和之后的数字成像,提供了作为特定分析物独特指纹的颜色变化图谱。使用包括主成分分析(PCA)和层次聚类分析(HCA)在内的统计和化学计量学方法,对生成的数字数据库进行了分析。在液相或气相实验中,通过比较颜色变化图谱,可以轻松识别特定的啤酒;使用HCA统计分析,识别错误率<3%。事实证明,即使是非常相似的啤酒之间的区分也很简单。此外,还证明了可以区分原始啤酒与加水或脱碳的影响。这些结果表明,比色传感器阵列可能被证明对啤酒以及其他饮料的质量保证/质量控制应用有用。