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Sensory profiling of aroma in Greek dry red wines using rank-rating and monadic scoring related to headspace composition

机译:使用与顶空成分相关的等级和单峰评分对希腊干红葡萄酒的香气进行感官分析

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摘要

Rank-rating and monadic scoring were compared in profiling sensory aroma character of 27 Greek dry red wines with 16 attributes. In parallel wine headspace volatiles were quantified using solid-phase micro-extraction gas chromatography but not identified. In rank-rating, 14 aroma attributes showed discriminations with P<0.05 and 11 P<0.001. In scoring, 6 of 16 attributes showed P<0.05. Principal component analysis (PCA) explained 88% variance in rank-rating data, with six significant components (PCs), in scoring 40% in two PCs. PCA analysis of 83 common flavour volatiles explained 48% variance in six PCs. Partial least-squares regression (PLS1) modelling achieved more and better models for attributes using rank-rating, 8 of 14, than for scoring, 3 of 16; PLS2 explained greater variance in rank-rating. For wine sensory/instrumental correlation studies, rank-rating has distinct advantages over monadic scoring in deciding volatiles contributing to sensory character prior to identification strategies such as HRGC–mass spectrometry.
机译:在对27种具有16个属性的希腊干红葡萄酒的感官香气特征进行分析时,比较了等级评分和单子得分。在平行的葡萄酒顶空,使用固相微萃取气相色谱对挥发物进行定量,但未鉴定。在等级评定中,有14种香气属性显示出P <0.05和11 P <0.001的辨别力。在评分中,16个属性中的6个显示P <0.05。主成分分析(PCA)解释了评级数据中88%的差异,其中有六个重要成分(PC),而在两个PC中得分为40%。对83种常见风味挥发物的PCA分析表明,六台PC的差异为48%。偏最小二乘回归(PLS1)建模使用等级评分(14分中的8分)比得分使用16分中的3分获得了更好的属性模型。 PLS2解释了等级评定中的较大差异。对于葡萄酒的感官/仪器相关性研究,在鉴定诸如HRGC-质谱之类的识别策略之前,等级评定在确定有助于感官特征的挥发物方面,优于单峰评分。

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